CN105069129A - Self-adaptive multi-label prediction method - Google Patents

Abstract

The present invention discloses a self-adaptive multi-label prediction method, which is characterized by comprising the following steps: 1. acquiring an initialization sample set; 2. acquiring a leader sample, an outsider sample, and an elector sample in the initialization sample set; 3. acquiring a cluster to which an elector sample set belongs; 4. performing coarse classification on a prediction sample by using a support vector machine; and 5. performing multi-label prediction on the prediction sample. According to the present invention, a label can be accurately added to network information, and accuracy, universality, interpretability and transferability of multi-label prediction can be improved, so as to implement intelligent information classification and processing in a big data environment.

Description

Self-adaptation many Tag Estimations method

Technical field

The invention belongs to intelligent information classification and process field, particularly relate to and a kind ofly can be applicable to the quick clustering of Multi-media information under large data environment and find self-adaptation many Tag Estimations method of density peaks point.

Background technology

Along with the fast development of network, quantity of information is just becoming geometric trend to increase, instantly microblogging, forum, micro-letter, Online Video, shopping at network and social networks all need the user friendly search of label and classification without exception, accurate and detailed label can allow user find needed for it rapidly on the one hand, businessman also can classify to user by label on the other hand, the product catering to its taste is recommended to different customer groups, thus avoid user because browsing a large amount of irrelevant information, valuable content is submerged in the ocean of information.If otherwise businessman cannot correct process information overload problem, will finally cause the continuous loss of consumer.

It is that independently single label carries out marking and marks how label converting for the sequence between label that the method at present adding label to information mainly contains many labels decomposition and inversion.Be converted into single label, ignored completely by the incidence relation between many labels, accuracy is low; Sequence between label not only needs a large amount of calculating, and after determining the sequence of label, also needs to determine the front label of this label further or rear label similarity degree is higher, therefore there is the not high defect of accuracy equally.

Compared to the present invention, there is following shortcoming in current disposal route:

1, the current network information is by the learning method of computing machine, the Forecasting Methodology made single label i.e. identification problem is more, but because many labels of information exist incidence relation, therefore utilize and decompose the method that many labels are single many labels, the accuracy of label is lower, can not reach practical purpose.

2, current many Tag Estimations technology often can only be handled it to given static data collection, as considered newly-added information, often needs to relearn, Reparametrization, can not accomplish automatically to adjust parameter with the change of data, therefore generalization is weak, and universality is poor.

3, processed by the order relation that many Tag Estimations of information transfer between label, not only need a large amount of calculating, and interpretation is poor, the accuracy of prediction is not high yet.

4, mostly existing many Tag Estimations technology is to improve a certain evaluate mark and design, and have ignored other standard, which results in the feature of its portable difference, the data centralization being only adapted at meeting some condition uses.

Summary of the invention

The present invention is the weak point existed to overcome prior art, a kind of self-adaptation many Tag Estimations method is provided, to label can be added to the network information exactly, improve the accuracy of many Tag Estimations, universality, interpretation and transferability, thus intelligent information classification and process under realizing large data environment.

The present invention is that technical solution problem adopts following technical scheme:

The feature of a kind of self-adaptation of the present invention many Tag Estimations method is carried out as follows:

Step 1: obtain initialization example set D:

Step 1.1, to be set up by the individual known object of num ' original illustration collection D '=inst ' ₁, inst ' ₂..., inst ' _a..., inst ' _{num '}, inst ' _arepresent the original illustration corresponding to a known object; 1≤a≤num '; And have inst ' _a=attr ' _a; Lab ' _a; Attr ' _arepresent the property set of described a known object feature; Lab ' _arepresent the tally set of described a known object semanteme; And have attr ' _a=attr ' _{a, 1}, attr ' _{a, 2}..., attr ' _a,n; Attr ' _a,nrepresent the n-th attribute of a known object; N is the attribute number of a known object; Lab ' _a=lab ' _{a, 1}, lab ' _{a, 2}..., lab ' _a,x..., lab ' _a,m; Lab ' _a,xrepresent an xth label of a known object; M is the number of tags of a known object; 1≤x≤m; And have: lab ' _a,x=1 represents that a known object semanteme meets an xth label; Lab ' _a,x=0 represents that a known object semanteme does not meet an xth label;

Step 1.2, to the property set of the num ' individual known object feature in described original illustration collection D ' attr ' ₁, attr ' ₂..., attr ' _a..., attr ' _{num '}be normalized respectively, obtain the individual known object feature of num ' after normalized property set attr " ₁, attr " ₂..., attr " _a..., attr " _{num '}; Property set arrt when a known object feature after described normalization " _awhen m corresponding label value is 0, delete the original illustration belonging to a known object after described normalization; Thus obtain the initialization example set D={inst of num example formation ₁, inst ₂..., inst _i..., inst _num; Inst _irepresent the example corresponding to i-th known object after initialization; And have inst _i={ attr _i; lab _i; Attr _irepresent the property set of i-th exemplary characteristics after initialization; lab _irepresent the tally set of described i-th exemplary semantic after initialization; 1≤i≤num;

Step 2: the clustering degree solving each example in described initialization example set D, thus determine the leader's example in initialization example set D, example not in the know and voter's example:

Step 2.1, using m label of each example in num example in described initialization example set D as m dimension coordinate, thus obtain i-th example inst _iwith a kth example inst _kthe Euclidean distance d of label _ik; 1≤k≤num and k ≠ i;

Step 2.2, definition iterations γ; And initialization γ=1; Define described i-th example inst _iaffiliated cluster be clu _i;

Step 2.3, formula (1) is utilized to obtain i-th example inst of the γ time iteration _ithe interior degree of polymerization thus obtain the interior degree of polymerization of num example of the γ time iteration and the degree of polymerization in maximum is designated as

${\mathrm{\ρ}}_i^{\left(\mathrm{\γ}\right)}=\underset{k=1}{\overset{num}{\Σ}}f(d_{ik}-d_c^{\left(\mathrm{\γ}\right)})---\left(1\right)$

In formula (1), it is the threshold value of the γ time iteration; When $d_{ik}\≤d_c^{\left(\mathrm{\γ}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\γ}\right)})=1;$ When $d_{ik}>d_c^{\left(\mathrm{\γ}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\γ}\right)})=0;$

Step 2.4, formula (2) or formula (3) is utilized to obtain i-th example inst of the γ time iteration _idiversity factor thus obtain the diversity factor of num example of the γ time iteration ${\mathrm{\δ}}^{\left(\mathrm{\γ}\right)}=\{{\mathrm{\δ}}_1^{\left(\mathrm{\γ}\right)},{\mathrm{\δ}}_2^{\left(\mathrm{\γ}\right)},...,{\mathrm{\δ}}_i^{\left(\mathrm{\γ}\right)},...,{\mathrm{\δ}}_{num}^{\left(\mathrm{\γ}\right)}\}:$

${\mathrm{\δ}}_i^{\left(\mathrm{\γ}\right)}=\underset{k=1}{\overset{num}{\Σ}}max\left(d_{ik}\right),$ When ${\mathrm{\ρ}}_i^{\left(\mathrm{\γ}\right)}={\mathrm{\ρ}}_{\max }^{\left(\mathrm{\γ}\right)}---\left(2\right)$

when ${\mathrm{\ρ}}_i^{\left(\mathrm{\γ}\right)}\≠{\mathrm{\ρ}}_{\max }^{\left(\mathrm{\γ}\right)}---\left(3\right)$

Step 2.5, diversity factor to num example of described the γ time iteration be normalized, obtain the diversity factor after normalization ${\mathrm{\δ}}^{\′\left(\mathrm{\γ}\right)}=\{{\mathrm{\δ}}_1^{\′\left(\mathrm{\γ}\right)},{\mathrm{\δ}}_2^{\′\left(\mathrm{\γ}\right)},...,{\mathrm{\δ}}_i^{\′\left(\mathrm{\γ}\right)},...,{\mathrm{\δ}}_{num}^{\′\left(\mathrm{\γ}\right)}\};$

Step 2.6, formula (4) is utilized to obtain i-th example inst of the γ time iteration _iclustering degree thus obtain the clustering degree of num example of the γ time iteration ${\mathrm{sco}}^{\left(\mathrm{\γ}\right)}=\{{\mathrm{sco}}_1^{\left(\mathrm{\γ}\right)},{\mathrm{sco}}_2^{\left(\mathrm{\γ}\right)},...,{\mathrm{sco}}_i^{\left(\mathrm{\γ}\right)}...,{\mathrm{sco}}_{num}^{\left(\mathrm{\γ}\right)}\}:$

${\mathrm{sco}}_i^{\left(\mathrm{\γ}\right)}={\mathrm{\ρ}}_i^{\left(\mathrm{\γ}\right)}\×{\mathrm{\δ}}_i^{\′\left(\mathrm{\γ}\right)}---\left(4\right)$

Step 2.7, clustering degree sco to num example of described the γ time iteration ^(γ)carry out descending sort, obtain clustering degree series and order and described clustering degree series sco ' ^(γ)the corresponding interior degree of polymerization is ${\mathrm{\ρ}}^{\′\left(\mathrm{\γ}\right)}=\{{\mathrm{\ρ}}_1^{\′\left(\mathrm{\γ}\right)},{\mathrm{\ρ}}_2^{\′\left(\mathrm{\γ}\right)},...,{\mathrm{\ρ}}_t^{\′\left(\mathrm{\γ}\right)},...,{\mathrm{\ρ}}_{num}^{\′\left(\mathrm{\γ}\right)}\};$ represent and work as ${\mathrm{sco}}_i^{\left(\mathrm{\γ}\right)}={\mathrm{sco}}_t^{\′\left(\mathrm{\γ}\right)}$ Time i-th example inst of the γ time iteration _ithe interior degree of polymerization; 1≤t≤num;

Step 2.8, initialization t=1;

Step 2.9, judgement and whether>=num × 3% is set up, if set up, then the threshold value of the γ time iteration for effective value, and after recording t, perform step 2.10; Otherwise, judge whether set up, if set up, then by t+1 assignment to t, and repeated execution of steps 2.9; Otherwise, amendment threshold value by γ+1 assignment to γ, and return execution step 2.3;

If i-th of step 2.10 the γ time iteration example inst _ithe interior degree of polymerization whether meet if meet, then described i-th example inst _ifor example not in the know, and make described i-th example inst _iaffiliated cluster clu _i=-1; Otherwise, judge whether set up, if set up, then i-th example inst _ifor leader's example, and make clu _i=i, otherwise, i-th example inst _ifor voter's example;

Step 2.11, add up the number of described leader's example and the number of described voter's example, and be designated as N and M respectively;

Step 2.12, remember that N number of leader's example set is 1≤α≤N; Then with described N number of leader example set D ^(l)the corresponding interior degree of polymerization is represent α leader's example the interior degree of polymerization; With described N number of leader example set D ^(l)corresponding tally set is ${\mathrm{lab}}^{\left(l\right)}=\{{\mathrm{lab}}_1^{\left(l\right)},{\mathrm{lab}}_2^{\left(l\right)},...,{\mathrm{lab}}_{\mathrm{\α}}^{\left(l\right)},...,{\mathrm{lab}}_N^{\left(l\right)}\};$ represent α leader's example tally set; With described N number of leader example set D ^(l)corresponding affiliated cluster is represent α leader's example affiliated cluster;

Step 2.13, note M voter's example set are 1≤β≤M; Then with described M voter example set D ^(v)the corresponding interior degree of polymerization is represent β voter's example the interior degree of polymerization; With described M voter example set D ^(v)corresponding tally set is ${\mathrm{lab}}^{\left(v\right)}=\{{\mathrm{lab}}_1^{\left(v\right)},{\mathrm{lab}}_2^{\left(v\right)},...,{\mathrm{lab}}_{\mathrm{\β}}^{\left(v\right)},...,{\mathrm{lab}}_M^{\left(v\right)}\};$ represent β voter's example tally set; With described M voter example set D ^(v)corresponding affiliated cluster is represent β voter's example affiliated cluster;

Step 3: obtain described M voter example set D ^(v)affiliated cluster clu ^(v):

Step 3.1, definition iterations χ; And initialization χ=1; And define z transfer example inst _z; Z>=0; And initialization α=1, β=1, z=0;

Step 3.2, from described N number of leader example set D ^(l)in choose wantonly α leader's example obtaining described α leader's example is with β voter's example of the χ time iteration the Euclidean distance of label

If step 3.3 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.3; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.4; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.11;

Step 3.4, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order represent β voter's example of the χ time iteration in subscript β _χ, tally set the interior degree of polymerization with affiliated cluster equal assignment gives z transfer example of the χ time iteration subscript, tally set, the interior degree of polymerization and affiliated cluster; And by β+1 assignment to β; Judge whether β≤M sets up, if set up, then perform step 3.3; Otherwise perform step 3.5;

If step 3.5 z≤0, then perform step 3.14; Otherwise, by χ+1 assignment to χ, and will assignment is given successively make β=1; And obtain β voter's example of described the χ time iteration with the χ time iteration z transfer example the Euclidean distance of label and by z-1 assignment to z;

If step 3.6 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.6; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.7; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.8;

Step 3.7, by z transfer example of the χ time iteration subscript z ^(χ)assignment is given and by z+1 assignment to z, order and by β+1 assignment to β; And judge whether β≤M sets up, if set up, then repeated execution of steps 3.6; Otherwise perform step 3.5;

Step 3.8, formula (5) is utilized to obtain β voter's example of the χ time iteration with the influence power of the existing leader's example of described the χ time iteration

${\mathrm{gra}}_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\ϵ}}^{\left(v\right)\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}=\frac{{\mathrm{\ρ}}_{{\mathrm{\β}}_{\mathrm{\χ}}}^{\left(v\right)}\×{\mathrm{\ρ}}_{\mathrm{\ϵ}}^{\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}}{d_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\ϵ}}^{\left(v\right)\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}}---\left(5\right)$

Step 3.9, formula (6) is utilized to obtain β voter's example of the χ time iteration with z transfer example of the χ time iteration influence power

${\mathrm{gra}}_{{\mathrm{\β}}_{\mathrm{\χ}}z}^{\left(v\right)\left(\mathrm{\χ}\right)}=\frac{{\mathrm{\ρ}}_{{\mathrm{\β}}_{\mathrm{\χ}}}^{\left(v\right)}\×{\mathrm{\ρ}}_z^{\left(\mathrm{\χ}\right)}}{d_{{\mathrm{\β}}_{\mathrm{\χ}}z}^{\left(v\right)\left(\mathrm{\χ}\right)}}---\left(6\right)$

If step 3.10 then by β+1 assignment to β, and perform step 3.6; Otherwise, order and by z+1 assignment to z, order and by β+1 assignment to β, and judge whether β≤M sets up, if set up, then perform step 3.6; Otherwise perform step 3.5;

Step 3.11, formula (7) is utilized to obtain β voter's example of the χ time iteration with the influence power of the existing leader's example of described the χ time iteration

${\mathrm{gra}}_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\ϵ}}^{\left(v\right)\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}=\frac{{\mathrm{\ρ}}_{{\mathrm{\β}}_{\mathrm{\χ}}}^{\left(v\right)}\×{\mathrm{\ρ}}_{\mathrm{\ϵ}}^{\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}}{d_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\ϵ}}^{\left(v\right)\left({\mathrm{\β}}_{\mathrm{\χ}}\right)}}---\left(7\right)$

Step 3.12, formula (8) is utilized to obtain β voter's example of the χ time iteration with α leader's example influence power

${\mathrm{gra}}_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\α}}^{\left(v\right)\left(l\right)}=\frac{{\mathrm{\ρ}}_{{\mathrm{\β}}_{\mathrm{\χ}}}^{\left(v\right)}\×{\mathrm{\ρ}}_{\mathrm{\α}}^{\left(l\right)}}{d_{{\mathrm{\β}}_{\mathrm{\χ}}\mathrm{\α}}^{\left(v\right)\left(l\right)}}---\left(8\right)$

If step 3.13 then by β+1 assignment to β, and perform step 3.3; Otherwise, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order and by β+1 assignment to β, and judge whether β≤M sets up, if set up, then perform step 3.3; Otherwise perform step 3.5;

Step 3.14, by α+1 assignment to α; And judge whether α≤N sets up, if set up, make β=1, and perform step 3.2; Otherwise perform step 3.15;

Step 3.15, by M voter example set D described during the χ time iteration ^(v)corresponding affiliated cluster assignment gives described M voter example set D successively ^(v)corresponding affiliated cluster

Step 3.16, to judge whether also to exist affiliated cluster be empty voter's example, if exist, then to arrange affiliated cluster be the value of the affiliated cluster of empty voter's example is-1;

Step 4; Support vector machine is adopted to carry out rough sort to prediction example:

4.1, the prediction example set P={instp be made up of nump prediction example is set up ₁, instp ₂..., instp _j..., instp _nump; Instp _jrepresent a jth prediction example; 1≤j≤nump; And have instp _j={ attrp _j; Labp _j; Arrtp _jrepresent a jth prediction example instp _jproperty set; Labp _jrepresent a jth prediction example instp _jtally set; Remember a described jth prediction example instp _jaffiliated cluster be clup _j;

4.2, with num the affiliated cluster { clu that described initialization example set D is corresponding ₁, clu ₂..., clu _i..., clu _numas training label, with the property set { attr of num known object in described initialization example set D ₁, attr ₂, attr _i..., attr _numas training sample; With nump the property set { attrp of described prediction example set P ₁, attrp ₂, attrp _j..., attrp _numpas forecast sample, and train with support vector machine method, obtain nump and predict label, give nump of described prediction example set P affiliated cluster by described nump prediction label difference assignment; Thus the rough sort completed described prediction example set P;

Step 5, to nump prediction example carry out many Tag Estimations;

Step 5.1, be upgrade example set the ψ time by nump exemplary integrated in num example in described initialization example set D and described prediction example set P $D_{new}^{\left(\mathrm{\ψ}\right)}=\{{\mathrm{inst}}_1,{\mathrm{inst}}_2,...,{\mathrm{inst}}_i,...,{\mathrm{inst}}_{num};{\mathrm{instp}}_1,{\mathrm{instp}}_2,...,{\mathrm{instp}}_j,...,{\mathrm{instp}}_{nump}\},$ Be designated as $D_{new}^{\left(\mathrm{\ψ}\right)}=\{{\mathrm{inst}}_1^{\left(\mathrm{\ψ}\right)},{\mathrm{inst}}_2^{\left(\mathrm{\ψ}\right)},...,{\mathrm{inst}}_{\mathrm{\Ω}}^{\left(\mathrm{\ψ}\right)},...,{\mathrm{inst}}_{num+nump}^{\left(\mathrm{\ψ}\right)}\};$ represent that Ω upgrades example the ψ time; 1≤Ω≤num+nump;

Step 5.2, described the ψ time renewal example set middle num+nump upgrade in example n attribute of each example respectively as n dimension coordinate, thus obtain Ω the ψ time renewal example example is upgraded the ψ time with ξ the Euclidean distance of attribute 1≤ξ≤num+nump and ξ ≠ Ω;

Step 5.3, formula (9) is utilized to obtain Ω the ψ time renewal example the attribute degree of polymerization thus obtain the attribute degree of polymerization of num+nump the renewal example upgraded for the ψ time

${\mathrm{\Γ}}_{\mathrm{\Ω}}^{\left(\mathrm{\ψ}\right)}=\underset{\mathrm{\ξ}=1}{\overset{num+nump}{\Σ}}f(d_{\mathrm{\Ω}\mathrm{\ξ}}^{\left(\mathrm{\ψ}\right)}-d_c^{\left(\mathrm{\γ}\right)})---\left(9\right)$

When $d_{\mathrm{\Ω}\mathrm{\ξ}}^{\left(\mathrm{\ψ}\right)}\≤d_c^{\left(\mathrm{\γ}\right)}$ Time, $f(d_{\mathrm{\Ω}\mathrm{\ξ}}^{\left(\mathrm{\ψ}\right)}-d_c^{\left(\mathrm{\γ}\right)})=1;$ When $d_{\mathrm{\Ω}\mathrm{\ξ}}^{\left(\mathrm{\ψ}\right)}>d_c^{\left(\mathrm{\γ}\right)}$ Time, $f(d_{\mathrm{\Ω\ξ}}^{\left(\mathrm{\ψ}\right)}-d_c^{\left(\mathrm{\γ}\right)})=0;$

Step 5.4, initialization j=1;

If a jth prediction example instp in the described prediction example set P of step 5.5 _jaffiliated cluster be clup _jwith i-th known example inst in described initialization example set D _iaffiliated cluster be clu _iidentical; Formula (10) is then utilized to obtain i-th known example inst _iexample instp is predicted with jth _jinfluence power gra _ij:

${\mathrm{gra}}_{ij}=\frac{{\mathrm{\Γ}}_i\×{\mathrm{\Γ}}_j}{d_{ij}}---\left(10\right)$

In formula (10), Γ _irepresent known example inst _iexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, Γ _jrepresent prediction example instp _jexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, d _ijrepresent described i-th known example inst _iexample instp is predicted with jth _jthe Euclidean distance of attribute;

Step 5.6, repetition step 5.5, thus obtain a jth prediction example instp _jwith the influence power of described other known example of initialization example set D, and record maximum effect power gra _max;

If step 5.7 gra _ij=gra _max, then labp is made _j=lab _i, represent the tally set labp of described prediction example set P _jin each label and the tally set lab of described initialization example set D _iin each label identical, thus obtain the prediction example of jth many Tag Estimations;

Step 5.8, by j+1 assignment to j, and judge whether j≤nump sets up, if set up, then return step 5.5 and perform, otherwise, has represented many Tag Estimations nump being predicted to example;

The feature of self-adaptation many Tag Estimations method of the present invention is:

In described step 5, also comprise step 5.9, by the described the ψ time renewal example set of tally set assignment to described correspondence completing the prediction example set P of many Tag Estimations in, thus obtain ψ+1 renewal example set example set is upgraded+1 time with described ψ the many Tag Estimations of self-adaptation are carried out as new initialization example set.

When occur new there is the prediction example of identical characteristics of objects and identical Object Semanteme time, only need can complete from step 4 and many Tag Estimations are carried out to new prediction example.

In described step 2.9, amendment threshold value rule be: if then will deduct τ ₂assignment is given otherwise, will add τ ₂assignment is given 0.1≤τ ₂≤ 0.5,75%≤τ ₁< 100%.

Compared with the prior art, beneficial effect of the present invention is embodied in:

1, the present invention adopts the method that first rough sort is precisely predicted again, by the adaptivity contained by the present invention, by taking turns iteration, prediction label is constantly evolved more, and then obtain and predict the outcome more accurately than existing many Tag Estimations technology, be a method can putting into practical application.

2, the present invention is by initialization example set, different initialization example set can be determined according to different known object characteristic sum semanteme, make the present invention can be widely used in the most applied environment of existing network platform, from simple literal data, to audio frequency, and even image, all can have and make Tag Estimation preferably, strong compared to prior art universality.

3, the present invention represents poly-degree in example by calculating the degree of polymerization in acquisition, by calculating the degree of coupling obtaining diversity factor and represent example, and according to the clustering degree that the interior degree of polymerization and diversity factor solve out, each parameter has physical meaning, take into full account the Data classification requirement of the low coupling of high cohesion, easy to understand and explanation, thus while ensure that the present invention has higher forecasting accuracy, make the present invention have stronger portability, many Tag Estimations can be carried out under various conditions.

4, the present invention accurately can find the leader's example in each product scope by the interior degree of polymerization; For microblogging, forum and social networks, can find the key user that in different topic field, influence power is maximum, by studying in great detail its behavior exactly by this method, measurable to the possible trend in this field, and recommend accurately for the user in this field provides.

5, the present invention is by influence power between sample calculation and example, not only may be used on many Tag Estimations, also can carry out analogy to the example of the known label of identical semanteme, look for the example very similar with many labels of this example, recommend user, improve the experience of user.

6, the present invention is when predicting that many labels of example are determined, adopts and chooses and predict that the tally set of the known example that example is the most similar is as the method for tally set predicting example, can recommend emerging prediction example by the customer group of this known example; Can be emerging product and find its market orientation comparatively accurately, and find potential user for it.

7, the present invention is owing to adopting the method prediction example completing many Tag Estimations being joined initialization example set, thus enriched existing training set, improve the accuracy of next round prediction, the present invention is made to have the learning ability of adaptivity, in the face of the example newly added can improve available data set further, with the increase of known label example, the accuracy of the method prediction will be improved further.

Embodiment

In the present embodiment, a kind of self-adaptation many Tag Estimations method is carried out as follows:

Step 1: obtain initialization example set D:

Step 1.1, to be set up by the individual known object of num ' original illustration collection D '=inst ' ₁, inst ' ₂..., inst ' _a..., inst ' _{num '}, inst ' _arepresent the original illustration corresponding to a known object; 1≤a≤num '; And have inst ' _a=attr ' _a; Lab ' _a; Attr ' _arepresent the property set of a known object feature; Lab ' _arepresent the tally set of a known object semanteme; And have attr ' _a=attr ' _{a, 1}, attr ' _{a, 2}..., attr ' _a,n; Attr ' _a,nrepresent the n-th attribute of a known object; N is the attribute number of a known object, lab ' _a=lab ' _{a, 1}, lab ' _{a, 2}..., lab ' _a,x..., lab ' _a,m; Lab ' _a,xrepresent an xth label of a known object; M is the number of tags of a known object; 1≤x≤m; And have: lab ' _a,x=1 represents that a known object semanteme meets an xth label; Lab ' _a,x=0 represents that a known object semanteme does not meet an xth label; Suppose, known object is picture, and by aberration, size etc. need the characteristics of objects described in detail as property set, by the value of accurate and detailed numeral as each attribute; By scenery picture, animal pictures etc. are non-be namely no Object Semanteme as tally set, represent with 0 and do not meet this label, represent with 1 and meet this label;

Step 1.2, to the property set of the individual known object feature of the num ' in original illustration collection D ' attr ' ₁, attr ' ₂..., attr ' _a..., attr ' _{num '}be normalized respectively; In normalized, with the property set attr ' of a known object feature _afor example, be namely first record attribute collection attr ' _{a, 1}, attr ' _{a, 2}..., attr ' _a,nthe maximum attribute attr ' of intermediate value _{a, max}, then with maximum attribute attr ' _{a, max}as denominator, carry out division calculation with attribute each in property set, just can obtain the property set attr of the known object feature after a normalized " _a; The rest may be inferred obtain the individual known object feature of num ' after normalized property set attr " ₁, attr " ₂..., attr " _a..., attr " _{num '}; Property set arrt when a known object feature after normalization " _awhen m corresponding label value is 0, delete the original illustration belonging to a known object after normalization; Thus obtain the initialization example set D={inst of num example formation ₁, inst ₂..., inst _i..., inst _num; Inst _irepresent the example corresponding to i-th known object after initialization; And have inst _i={ attr _i; lab _i; Attr _irepresent the property set of i-th exemplary characteristics after initialization; lab _irepresent the tally set of i-th exemplary semantic after initialization; 1≤i≤num; As shown in table 1:

Table 1: initialization example set D i-th example inst _itables of data

attr i,1

…

attr i,n

lab i,1

…

lab i,m

ρ i

δ i

sco i

clu i

inst i

Step 2: the clustering degree solving each example in initialization example set D, thus determine the leader's example in initialization example set D, example not in the know and voter's example:

Step 2.1, using m label of each example in num example in initialization example set D as m dimension coordinate, thus obtain i-th example inst _iwith a kth example inst _kthe Euclidean distance d of label _ik; 1≤k≤num and k ≠ i; Such as, the Euclidean distance d of first example and second example tag is solved ₁₂, first example and second example have the label of m same names, but due to value not necessarily identical, be then expressed as the tally set lab of first example ₁={ lab _1,1, lab _1,2..., lab _{1, m}and the tally set lab of second example ₂={ lab _2,1, lab _2,2..., lab _{2, m}, then the Euclidean distance d of label ₁₂for $d_{12}=\sqrt{{({\mathrm{lab}}_{1,1}-{\mathrm{lab}}_{2,1})}^2+\mathrm{...}+{({\mathrm{lab}}_{1,m}-{\mathrm{lab}}_{2,m})}^2};$

Step 2.2, definition iterations γ; And initialization γ=1; Define i-th example inst _iaffiliated cluster be clu _i;

Step 2.3, formula (1) is utilized to obtain i-th example inst of the γ time iteration _ithe interior degree of polymerization thus obtain the interior degree of polymerization of num example of the γ time iteration and the degree of polymerization in maximum is designated as

${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}=\underset{k=1}{\overset{num}{\&Sigma;}}f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})---\left(1\right)$

In formula (1), it is the threshold value of the γ time iteration; When $d_{\mathrm{ik}}\&le;d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})=1;$ When $d_{ik}>d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})=0;$

Step 2.4, formula (2) or formula (3) is utilized to obtain i-th example inst of the γ time iteration _idiversity factor thus obtain the diversity factor of num example of the γ time iteration ${\mathrm{\&delta;}}^{\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&delta;}}_1^{\left(\mathrm{\&gamma;}\right)},{\mathrm{\&delta;}}_2^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_i^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_{num}^{\left(\mathrm{\&gamma;}\right)}\}:$

${\mathrm{\&delta;}}_i^{\left(\mathrm{\&gamma;}\right)}=\underset{k=1}{\overset{num}{\&Sigma;}}max\left(d_{ik}\right),$ When ${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{\&rho;}}_{\max }^{\left(\mathrm{\&gamma;}\right)}---\left(2\right)$

when ${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}\&NotEqual;{\mathrm{\&rho;}}_{\max }^{\left(\mathrm{\&gamma;}\right)}---\left(3\right)$

Step 2.5, diversity factor to num example of the γ time iteration be normalized, obtain the diversity factor after normalization the diversity factor after normalization will be made by step 2.4 and step 2.5 have larger differentiation, make minority close to 1, major part value is all less than 0.5, and this will contribute to choosing of leader's example;

Step 2.6, formula (4) is utilized to obtain i-th example inst of the γ time iteration _iclustering degree thus obtain the clustering degree of num example of the γ time iteration ${\mathrm{sco}}^{\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{sco}}_1^{\left(\mathrm{\&gamma;}\right)},{\mathrm{sco}}_2^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}...,{\mathrm{sco}}_{num}^{\left(\mathrm{\&gamma;}\right)}\}:$

${\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}\&times;{\mathrm{\&delta;}}_i^{\&prime;\left(\mathrm{\&gamma;}\right)}---\left(4\right)$

Step 2.7, clustering degree sco to num example of the γ time iteration ^(γ)carry out descending sort, obtain clustering degree series and order and clustering degree series sco ' ^(γ)the corresponding interior degree of polymerization is ${\mathrm{\&rho;}}^{\&prime;\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&rho;}}_1^{\&prime;\left(\mathrm{\&gamma;}\right)},{\mathrm{\&rho;}}_2^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_t^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_{num}^{\&prime;\left(\mathrm{\&gamma;}\right)}\};$ represent and work as ${\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{sco}}_t^{\&prime;\left(\mathrm{\&gamma;}\right)}$ Time i-th example inst of the γ time iteration _ithe interior degree of polymerization; 1≤t≤num;

Step 2.8, initialization t=1;

Step 2.9, judgement and whether>=num × 3% is set up, if set up, then the threshold value of the γ time iteration for effective value, and after recording t, perform step 2.10; Otherwise, judge whether set up, if set up, then by t+1 assignment to t, and repeated execution of steps 2.9; Otherwise, amendment threshold value amendment threshold value rule be: if then will deduct τ ₂assignment is given otherwise, will add τ ₂assignment is given 0.1≤τ ₂≤ 0.5,75%≤τ ₁< 100%; By γ+1 assignment to γ, and return execution step 2.3; Judge and in the condition of>=num × 3%, 1.25 and 3% is not changeless, the present invention is that to be based upon example numbers be ten thousand grades, number of tags is below 20, have more excellent solution, when example numbers and number of tags change time, can take the circumstances into consideration to modify, its principle can ensure only to choose a small amount of example of clustering degree much larger than other example in step below as leader's example;

If i-th of step 2.10 the γ time iteration example inst _ithe interior degree of polymerization whether meet if meet, then i-th example inst _ifor example not in the know, and make i-th example inst _iaffiliated cluster clu _i=-1; Otherwise, judge whether set up, if set up, then i-th example inst _ifor leader's example, and make clu _i=i, otherwise, i-th example inst _ifor voter's example;

Step 2.11, the number of statistics leader example and the number of voter's example, and be designated as N and M respectively;

Step 2.12, remember that N number of leader's example set is 1≤α≤N; Then with N number of leader example set D ^(l)the corresponding interior degree of polymerization is represent α leader's example the interior degree of polymerization; With N number of leader example set D ^(l)corresponding tally set is ${\mathrm{lab}}^{\left(l\right)}=\{{\mathrm{lab}}_1^{\left(l\right)},{\mathrm{lab}}_2^{\left(l\right)},...,{\mathrm{lab}}_{\mathrm{\&alpha;}}^{\left(l\right)},...,{\mathrm{lab}}_N^{\left(l\right)}\};$ represent α leader's example tally set; With N number of leader example set D ^(l)corresponding affiliated cluster is represent α leader's example affiliated cluster;

Step 2.13, note M voter's example set are 1≤β≤M; Then with M voter example set D ^(v)the corresponding interior degree of polymerization is represent β voter's example the interior degree of polymerization; With M voter example set D ^(v)corresponding tally set is ${\mathrm{lab}}^{\left(v\right)}=\{{\mathrm{lab}}_1^{\left(v\right)},{\mathrm{lab}}_2^{\left(v\right)},...,{\mathrm{lab}}_{\mathrm{\&beta;}}^{\left(v\right)},...,{\mathrm{lab}}_M^{\left(v\right)}\};$ represent β voter's example tally set; With M voter example set D ^(v)corresponding affiliated cluster is represent β voter's example affiliated cluster;

Step 3: obtain M voter example set D ^(v)affiliated cluster clu ^(v):

Step 3.1, definition iterations χ; And initialization χ=1; And define z transfer example inst _z; Z>=0; And initialization α=1, β=1, z=0; Z transfer example inst _zstorage organization is similar to conventional stack architecture, and the present invention is clear in order to state, and introduces iterations χ simultaneously, be used for distinguishing z identical time transfer example; Now M voter example set D ^(v)corresponding affiliated cluster value be all sky;

Step 3.2, from N number of leader example set D ^(l)in choose wantonly α leader's example obtaining α leader's example is with β voter's example of the χ time iteration the Euclidean distance of label

If step 3.3 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.3; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.4; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.11; Such as, the χ time existing leader's example of iteration is inst ₉, then

Step 3.4, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order represent β voter's example of the χ time iteration in subscript β _χ, tally set the interior degree of polymerization with affiliated cluster equal assignment gives z transfer example of the χ time iteration subscript, tally set, the interior degree of polymerization and affiliated cluster; And by β+1 assignment to β; Judge whether β≤M sets up, if set up, then perform step 3.3; Otherwise perform step 3.5; represent that an example has equaled another example, it only represents that value corresponding to these two examples is identical, by the subscript of example on the right of equal sign, tally set, the interior degree of polymerization and affiliated cluster assignment to the subscript of equal sign left side example, tally set, the interior degree of polymerization and affiliated cluster;

If step 3.5 z≤0, then perform step 3.14; Otherwise, by χ+1 assignment to χ, and will assignment is given successively for the parameter that other is relevant to χ, the assignment associated by χ-1 is also needed to associate to corresponding χ, to keep the coherent of data and consistance, such as make β=1; And obtain β voter's example of described the χ time iteration with the χ time iteration z transfer example the Euclidean distance of label and by z-1 assignment to z;

If step 3.6 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.6; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.7; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.8;

Step 3.7, by z transfer example of the χ time iteration subscript z ^(χ)assignment is given and by z+1 assignment to z, order and by β+1 assignment to β; And judge whether β≤M sets up, if set up, then repeated execution of steps 3.6; Otherwise perform step 3.5;

Step 3.8, formula (5) is utilized to obtain β voter's example of the χ time iteration with the influence power of the χ time existing leader's example of iteration

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&epsiv;}}^{\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}---\left(5\right)$

Formula (5) extends to the calculating of the influence power calculating wantonly one or two semantic identical example, only need to know the interior degree of polymerization of two examples and the Euclidean distance of both labels, or the Euclidean distance of the attribute degree of polymerization of two examples and both attributes, apply mechanically formula (5), just can obtain the influence power between two examples;

Step 3.9, formula (6) is utilized to obtain β voter's example of the χ time iteration with z transfer example of the χ time iteration influence power

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}z}^{\left(v\right)\left(\mathrm{\&chi;}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_z^{\left(\mathrm{\&chi;}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}z}^{\left(v\right)\left(\mathrm{\&chi;}\right)}}---\left(6\right)$

If step 3.10 then by β+1 assignment to β, and perform step 3.6; Otherwise, order and by z+1 assignment to z, order and by β+1 assignment to β, and judge whether β≤M sets up, if set up, then perform step 3.6; Otherwise perform step 3.5;

Step 3.11, formula (7) is utilized to obtain β voter's example of the χ time iteration with the influence power of the χ time existing leader's example of iteration

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&epsiv;}}^{\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}---\left(7\right)$

Step 3.12, formula (8) is utilized to obtain β voter's example of the χ time iteration with α leader's example influence power

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&alpha;}}^{\left(v\right)\left(l\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&alpha;}}^{\left(l\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&alpha;}}^{\left(v\right)\left(l\right)}}---\left(8\right)$

If step 3.13 then by β+1 assignment to β, and perform step 3.3; Otherwise, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order and judge whether β≤M sets up, if set up, then by β+1 assignment to β, and perform step 3.3; Otherwise perform step 3.5;

Step 3.14, by α+1 assignment to α; And judge whether α≤N sets up, if set up, make β=1, and perform step 3.2; Otherwise, perform step 3.15;

Step 3.15, by M during the χ time iteration voter example set D ^(v)corresponding affiliated cluster assignment is to M voter example set D successively ^(v)corresponding affiliated cluster

Step 3.16, to judge whether also to exist affiliated cluster be empty voter's example, if exist, then to arrange affiliated cluster be the value of the affiliated cluster of empty voter's example is-1; Therefore, the number of the value that the affiliated cluster of voter's example is desirable is N+1, and the value of the affiliated cluster of corresponding N number of leader's example and affiliated cluster are the situation of-1 respectively;

Step 4; Support vector machine is adopted to carry out rough sort to prediction example:

4.1, the prediction example set P={instp be made up of nump prediction example is set up ₁, instp ₂..., instp _j..., instp _nump; Instp _jrepresent a jth prediction example; 1≤j≤nump; And have instp _j={ attrp _j; Labp _j; Arrtp _jrepresent a jth prediction example instp _jproperty set; Labp _jrepresent a jth prediction example instp _jtally set; A note jth prediction example instp _jaffiliated cluster be clup _j; Predict in the present invention that example and known example must be same targets, namely the characteristic sum semanteme of object is identical, such as, known example is picture, then predict that example also needs to be picture, all by aberration, sizes etc. need the characteristics of objects described in detail as property set, by scenery picture, animal pictures etc. are non-be namely no Object Semanteme as tally set, two example set have property set and the tally set of same names, but are worth different, clear for stating, the present invention distinguishes with distinct symbols when discussing;

4.2, with num the affiliated cluster { clu that initialization example set D is corresponding ₁, clu ₂..., clu _i..., clu _numas training label, with the property set { attr of the known object of the num in initialization example set D ₁, attr ₂, attr _i..., attr _numas training sample; To predict nump the property set { attrp of example set P ₁, attrp ₂, attrp _j..., attrp _numpas forecast sample, and train with support vector machine method, obtain nump prediction label, give cluster belonging to nump that predicts example set P by nump prediction label difference assignment; Thus the rough sort completed prediction example set P; Support vector machine method has three inputs usually, is respectively training label, training sample and forecast sample, thus obtains an output, namely predicts label;

Step 5, to nump prediction example carry out many Tag Estimations;

Step 5.1, be upgrade example set the ψ time by nump exemplary integrated in num example in described initialization example set D and described prediction example set P $D_{new}^{\left(\mathrm{\&psi;}\right)}=\{{\mathrm{inst}}_1,{\mathrm{inst}}_2,...,{\mathrm{inst}}_i,...,{\mathrm{inst}}_{num};{\mathrm{instp}}_1,{\mathrm{instp}}_2,...,{\mathrm{instp}}_j,...,{\mathrm{instp}}_{nump}\},$ Be designated as $D_{new}^{\left(\mathrm{\&psi;}\right)}=\{{\mathrm{inst}}_1^{\left(\mathrm{\&psi;}\right)},{\mathrm{inst}}_2^{\left(\mathrm{\&psi;}\right)},...,{\mathrm{inst}}_{\mathrm{\&Omega;}}^{\left(\mathrm{\&psi;}\right)},...,{\mathrm{inst}}_{num+nump}^{\left(\mathrm{\&psi;}\right)}\};$ represent that Ω upgrades example the ψ time; 1≤Ω≤num+nump; ψ is update times, upgrade mainly to comprise and existing initialization example is become an example set with prediction exemplary integrated, and the ψ time of the tally set assignment to described correspondence that complete the prediction example set P of many Tag Estimations is upgraded in example set, ψ is initialized as 1, often complete after once upgrading, by ψ+1 assignment to ψ;

Step 5.2, described the ψ time renewal example set middle num+nump upgrade in example n attribute of each example respectively as n dimension coordinate, thus obtain Ω the ψ time renewal example example is upgraded the ψ time with ξ the Euclidean distance of attribute 1≤ξ≤num+nump and ξ ≠ Ω;

Step 5.3, formula (9) is utilized to obtain Ω the ψ time renewal example the attribute degree of polymerization thus obtain the attribute degree of polymerization of num+nump the renewal example upgraded for the ψ time

${\mathrm{\&Gamma;}}_{\mathrm{\&Omega;}}^{\left(\mathrm{\&psi;}\right)}=\underset{\mathrm{\&xi;}=1}{\overset{num+nump}{\&Sigma;}}f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})---\left(9\right)$

When $d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}\&le;d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})=1;$ When $d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}>d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})=0;$ Solve attribute degree of polymerization formula and interior degree of polymerization formula is similar to, but become the Euclidean distance of attribute by the Euclidean distance of label;

Step 5.4, initialization j=1;

If a jth prediction example instp in the described prediction example set P of step 5.5 _jaffiliated cluster be clup _jwith i-th known example inst in described initialization example set D _iaffiliated cluster be clu _iidentical; Formula (10) is then utilized to obtain i-th known example inst _iexample instp is predicted with jth _jinfluence power gra _ij:

${\mathrm{gra}}_{ij}=\frac{{\mathrm{\&Gamma;}}_i\&times;{\mathrm{\&Gamma;}}_j}{d_{ij}}---\left(10\right)$

In formula (10), Γ _irepresent known example inst _iexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, Γ _jrepresent prediction example instp _jexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, d _ijrepresent described i-th known example inst _iexample instp is predicted with jth _jthe Euclidean distance of attribute;

Step 5.6, repetition step 5.5, thus obtain a jth prediction example instp _jwith the influence power of described other known example of initialization example set D, and record maximum effect power gra _max;

If step 5.7 gra _ij=gra _max, then labp is made _j=lab _i, represent the tally set labp of described prediction example set P _jin each label and the tally set lab of described initialization example set D _iin each label identical, thus obtain the prediction example of jth many Tag Estimations;

Step 5.8, by j+1 assignment to j, and judge whether j≤nump sets up, if set up, then return step 5.5 and perform, otherwise, has represented many Tag Estimations nump being predicted to example;

Step 5.9, the described tally set assignment completing the prediction example set P of many Tag Estimations is upgraded example set the ψ time to described correspondence in, thus obtain ψ+1 renewal example set example set is upgraded+1 time with described ψ the many Tag Estimations of self-adaptation are carried out as new initialization example set, thus enrich existing training set, improve the accuracy of next round prediction, when occur new there is the prediction example of identical characteristics of objects and identical Object Semanteme time, only need can complete from step 4 and many Tag Estimations are carried out to new prediction example.

Claims (4)

1. self-adaptation many Tag Estimations method, is characterized in that carrying out as follows:

Step 1: obtain initialization example set D:

Step 1.1, to be set up by the individual known object of num ' original illustration collection D '=inst ' ₁, inst ' ₂..., inst ' _a..., inst ' _{num '}, inst ' _arepresent the original illustration corresponding to a known object; 1≤a≤num '; And have inst ' _a=attr ' _a; Lab ' _a; Attr ' _arepresent the property set of described a known object feature; Lab ' _arepresent the tally set of described a known object semanteme; And have attr ' _a=attr ' _{a, 1}, attr ' _{a, 2}..., attr ' _a,n; Attr ' _a,nrepresent the n-th attribute of a known object; N is the attribute number of a known object; Lab ' _a=lab ' _{a, 1}, lab ' _{a, 2}..., lab ' _a,x..., lab ' _a,m; Lab ' _a,xrepresent an xth label of a known object; M is the number of tags of a known object; 1≤x≤m; And have: lab ' _a,x=1 represents that a known object semanteme meets an xth label; Lab ' _a,x=0 represents that a known object semanteme does not meet an xth label;

Step 1.2, to the property set of the num ' individual known object feature in described original illustration collection D ' attr ' ₁, attr ' ₂..., attr ' _a..., attr ' _{num '}be normalized respectively, obtain the individual known object feature of num ' after normalized property set attr " ₁, attr " ₂..., attr " _a..., attr " _{num '}; As the property set arrt of a known object feature after described normalization _a" when m corresponding label value is 0, delete the original illustration belonging to a known object after described normalization; Thus obtain the initialization example set D={inst of num example formation ₁, inst ₂..., inst _i..., inst _num; Inst _irepresent the example corresponding to i-th known object after initialization; And have inst _i={ attr _i; lab _i; Attr _irepresent the property set of i-th exemplary characteristics after initialization; lab _irepresent the tally set of described i-th exemplary semantic after initialization; 1≤i≤num;

Step 2: the clustering degree solving each example in described initialization example set D, thus determine the leader's example in initialization example set D, example not in the know and voter's example:

Step 2.1, using m label of each example in num example in described initialization example set D as m dimension coordinate, thus obtain i-th example inst _iwith a kth example inst _kthe Euclidean distance d of label _ik; 1≤k≤num and k ≠ i;

Step 2.2, definition iterations γ; And initialization γ=1; Define described i-th example inst _iaffiliated cluster be clu _i;

Step 2.3, formula (1) is utilized to obtain i-th example inst of the γ time iteration _ithe interior degree of polymerization thus obtain the interior degree of polymerization of num example of the γ time iteration and the degree of polymerization in maximum is designated as

${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}=\underset{k=1}{\overset{num}{\&Sigma;}}f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})---\left(1\right)$

In formula (1), it is the threshold value of the γ time iteration; When $d_{ik}\&le;d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})=1;$ When $d_{ik}>d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{ik}-d_c^{\left(\mathrm{\&gamma;}\right)})=0;$

Step 2.4, formula (2) or formula (3) is utilized to obtain i-th example inst of the γ time iteration _idiversity factor thus obtain the diversity factor of num example of the γ time iteration ${\mathrm{\&delta;}}^{\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&delta;}}_1^{\left(\mathrm{\&gamma;}\right)},{\mathrm{\&delta;}}_2^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_i^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_{num}^{\left(\mathrm{\&gamma;}\right)}\}:$

${\mathrm{\&delta;}}_i^{\left(\mathrm{\&gamma;}\right)}=\underset{k=1}{\overset{num}{\&Sigma;}}max\left(d_{ik}\right),$ When ${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{\&rho;}}_{\max }^{\left(\mathrm{\&gamma;}\right)}---\left(2\right)$

when ${\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}\&NotEqual;{\mathrm{\&rho;}}_{\max }^{\left(\mathrm{\&gamma;}\right)}---\left(3\right)$

Step 2.5, diversity factor δ to num example of described the γ time iteration ^(γ)be normalized, obtain the diversity factor after normalization ${\mathrm{\&delta;}}^{\&prime;\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&delta;}}_1^{\&prime;\left(\mathrm{\&gamma;}\right)},{\mathrm{\&delta;}}_2^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_i^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&delta;}}_{num}^{\&prime;\left(\mathrm{\&gamma;}\right)}\};$

Step 2.6, formula (4) is utilized to obtain i-th example inst of the γ time iteration _iclustering degree thus obtain the clustering degree of num example of the γ time iteration ${\mathrm{sco}}^{\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{sco}}_1^{\left(\mathrm{\&gamma;}\right)},{\mathrm{sco}}_2^{\left(\mathrm{\&gamma;}\right)},...,{\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}...,{\mathrm{sco}}_{num}^{\left(\mathrm{\&gamma;}\right)}\}:$

${\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{\&rho;}}_i^{\left(\mathrm{\&gamma;}\right)}\&times;{\mathrm{\&delta;}}_i^{\&prime;\left(\mathrm{\&gamma;}\right)}---\left(4\right)$

Step 2.7, clustering degree sco to num example of described the γ time iteration ^(γ)carry out descending sort, obtain clustering degree series and order and described clustering degree series sco ' ^(γ)the corresponding interior degree of polymerization is ${\mathrm{\&rho;}}^{\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&rho;}}_1^{\&prime;\left(\mathrm{\&gamma;}\right)},{\mathrm{\&rho;}}_2^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_t^{\&prime;\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_{num}^{\&prime;\left(\mathrm{\&gamma;}\right)}\};$ represent and work as ${\mathrm{sco}}_i^{\left(\mathrm{\&gamma;}\right)}={\mathrm{sco}}_t^{\&prime;\left(\mathrm{\&gamma;}\right)}$ Time i-th example inst of the γ time iteration _ithe interior degree of polymerization; 1≤t≤num;

Step 2.8, initialization t=1;

Step 2.9, judgement and whether set up, if set up, then the threshold value of the γ time iteration for effective value, and after recording t, perform step 2.10; Otherwise, judge whether set up, if set up, then by t+1 assignment to t, and repeated execution of steps 2.9; Otherwise, amendment threshold value by γ+1 assignment to γ, and return execution step 2.3;

If i-th of step 2.10 the γ time iteration example inst _ithe interior degree of polymerization whether meet if meet, then described i-th example inst _ifor example not in the know, and make described i-th example inst _iaffiliated cluster clu _i=-1; Otherwise, judge whether set up, if set up, then i-th example inst _ifor leader's example, and make clu _i=i, otherwise, i-th example inst _ifor voter's example;

Step 2.11, add up the number of described leader's example and the number of described voter's example, and be designated as N and M respectively;

Step 2.12, remember that N number of leader's example set is $D^{\left(l\right)}=\{{\mathrm{inst}}_1^{\left(l\right)},{\mathrm{inst}}_2^{\left(l\right)},...,{\mathrm{inst}}_{\mathrm{\&alpha;}}^{\left(l\right)},...,{\mathrm{inst}}_N^{\left(l\right)}\},$ 1≤α≤N; Then with described N number of leader example set D ^(l)the corresponding interior degree of polymerization is represent α leader's example the interior degree of polymerization; With described N number of leader example set D ^(l)corresponding tally set is ${\mathrm{lab}}^{\left(l\right)}=\{{\mathrm{lab}}_1^{\left(l\right)},{\mathrm{lab}}_2^{\left(l\right)},...,{\mathrm{lab}}_{\mathrm{\&alpha;}}^{\left(l\right)},...,{\mathrm{lab}}_N^{\left(l\right)}\};$ represent α leader's example tally set; With described N number of leader example set D ^(l)corresponding affiliated cluster is ${\mathrm{clu}}^{\left(l\right)}=\{{\mathrm{clu}}_1^{\left(l\right)},{\mathrm{clu}}_2^{\left(l\right)},...,{\mathrm{clu}}_{\mathrm{\&alpha;}}^{\left(l\right)},...,{\mathrm{clu}}_N^{\left(l\right)}\};$ represent α leader's example affiliated cluster;

Step 2.13, note M voter's example set are $D^{\left(v\right)}=\{{\mathrm{inst}}_1^{\left(v\right)},{\mathrm{inst}}_2^{\left(v\right)},...,{\mathrm{inst}}_{\mathrm{\&beta;}}^{\left(v\right)},...,{\mathrm{inst}}_M^{\left(v\right)}\},$ 1≤β≤M; Then with described M voter example set D ^(v)the corresponding interior degree of polymerization is ${\mathrm{\&rho;}}^{\left(v\right)\left(\mathrm{\&gamma;}\right)}=\{{\mathrm{\&rho;}}_1^{\left(v\right)\left(\mathrm{\&gamma;}\right)},{\mathrm{\&rho;}}_2^{\left(v\right)\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_{\mathrm{\&beta;}}^{\left(v\right)\left(\mathrm{\&gamma;}\right)},...,{\mathrm{\&rho;}}_M^{\left(v\right)\left(\mathrm{\&gamma;}\right)}\};$ represent β voter's example the interior degree of polymerization; With described M voter example set D ^(v)corresponding tally set is ${\mathrm{lab}}^{\left(v\right)}=\{{\mathrm{lab}}_1^{\left(v\right)},{\mathrm{lab}}_2^{\left(v\right)},...,{\mathrm{lab}}_{\mathrm{\&beta;}}^{\left(v\right)},...,{\mathrm{lab}}_M^{\left(v\right)}\};$ represent β voter's example tally set; With described M voter example set D ^(v)corresponding affiliated cluster is ${\mathrm{clu}}^{\left(v\right)}=\{{\mathrm{clu}}_1^{\left(v\right)},{\mathrm{clu}}_2^{\left(v\right)},...,{\mathrm{clu}}_{\mathrm{\&beta;}}^{\left(v\right)},\mathrm{...},{\mathrm{clu}}_M^{\left(u\right)}\};$ represent β voter's example affiliated cluster;

Step 3: obtain described M voter example set D ^(v)affiliated cluster clu ^(v):

Step 3.1, definition iterations χ; And initialization χ=1; And define z transfer example inst _z; Z>=0; And initialization α=1, β=1, z=0;

Step 3.2, from described N number of leader example set D ^(l)in choose wantonly α leader's example obtaining described α leader's example is with β voter's example of the χ time iteration the Euclidean distance of label

If step 3.3 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.3; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.4; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.11;

Step 3.4, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order represent β voter's example of the χ time iteration in subscript β _χ, tally set the interior degree of polymerization with affiliated cluster equal assignment gives z transfer example of the χ time iteration subscript, tally set, the interior degree of polymerization and affiliated cluster; And by β+1 assignment to β; Judge whether β≤M sets up, if set up, then perform step 3.3; Otherwise perform step 3.5;

If step 3.5 z≤0, then perform step 3.14; Otherwise, by χ+1 assignment to χ, and will assignment is given successively make β=1; And obtain β voter's example of described the χ time iteration with the χ time iteration z transfer example the Euclidean distance of label and by z-1 assignment to z;

If step 3.6 time, then by β+1 assignment to β, and judge whether β≤M sets up, if set up, repeated execution of steps 3.6; Otherwise perform step 3.5; If time, judge β voter's example of the χ time iteration affiliated cluster whether be empty, if it is empty, then perform step 3.7; Otherwise, represent β voter's example of the χ time iteration affiliated cluster value be the subscript of the χ time existing leader's example of iteration, be designated as perform step 3.8;

Step 3.7, by z transfer example of the χ time iteration subscript z ^(χ)assignment is given and by z+1 assignment to z, order and by β+1 assignment to β; And judge whether β≤M sets up, if set up, then repeated execution of steps 3.6; Otherwise perform step 3.5;

Step 3.8, formula (5) is utilized to obtain β voter's example of the χ time iteration with the influence power of the existing leader's example of described the χ time iteration

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&epsiv;}}^{\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}---\left(5\right)$

Step 3.9, formula (6) is utilized to obtain β voter's example of the χ time iteration with z transfer example of the χ time iteration influence power

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}z}^{\left(v\right)\left(\mathrm{\&chi;}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_z^{\left(\mathrm{\&chi;}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}z}^{\left(v\right)\left(\mathrm{\&chi;}\right)}}---\left(6\right)$

If step 3.10 then by β+1 assignment to β, and perform step 3.6; Otherwise, order and by z+1 assignment to z, order and by β+1 assignment to β, and judge whether β≤M sets up, if set up, then perform step 3.6; Otherwise perform step 3.5;

Step 3.11, formula (7) is utilized to obtain β voter's example of the χ time iteration with the influence power of the existing leader's example of described the χ time iteration

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&epsiv;}}^{\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&epsiv;}}^{\left(v\right)\left({\mathrm{\&beta;}}_{\mathrm{\&chi;}}\right)}}---\left(7\right)$

Step 3.12, formula (8) is utilized to obtain β voter's example of the χ time iteration with α leader's example influence power

${\mathrm{gra}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&alpha;}}^{\left(v\right)\left(l\right)}=\frac{{\mathrm{\&rho;}}_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}}^{\left(v\right)}\&times;{\mathrm{\&rho;}}_{\mathrm{\&alpha;}}^{\left(l\right)}}{d_{{\mathrm{\&beta;}}_{\mathrm{\&chi;}}\mathrm{\&alpha;}}^{\left(v\right)\left(l\right)}}---\left(8\right)$

If step 3.13 then by β+1 assignment to β, and perform step 3.3; Otherwise, by α leader's example subscript α ^(l)assignment is given and by z+1 assignment to z, order and by β+1 assignment to β, and judge whether β≤M sets up, if set up, then perform step 3.3; Otherwise perform step 3.5;

Step 3.14, by α+1 assignment to α; And judge whether α≤N sets up, if set up, make β=1, and perform step 3.2; Otherwise perform step 3.15;

Step 3.15, by M voter example set D described during the χ time iteration ^(v)corresponding affiliated cluster assignment gives described M voter example set D successively ^(v)corresponding affiliated cluster $\{{\mathrm{clu}}_1^{\left(v\right)},{\mathrm{clu}}_2^{\left(v\right)},...,{\mathrm{clu}}_{\mathrm{\&beta;}}^{\left(v\right)},\mathrm{...},{\mathrm{clu}}_M^{\left(v\right)}\};$

Step 3.16, to judge whether also to exist affiliated cluster be empty voter's example, if exist, then to arrange affiliated cluster be the value of the affiliated cluster of empty voter's example is-1;

Step 4; Support vector machine is adopted to carry out rough sort to prediction example:

4.1, the prediction example set P={instp be made up of nump prediction example is set up ₁, instp ₂..., instp _j..., instp _nump; Instp _jrepresent a jth prediction example; 1≤j≤nump; And have instp _j={ attrp _j; Labp _j; Arrtp _jrepresent a jth prediction example instp _jproperty set; Labp _jrepresent a jth prediction example instp _jtally set; Remember a described jth prediction example instp _jaffiliated cluster be clup _j;

4.2, with num the affiliated cluster { clu that described initialization example set D is corresponding ₁, clu ₂..., clu _i..., clu _numas training label, with the property set { attr of num known object in described initialization example set D ₁, attr ₂, attr _i..., attr _numas training sample; With nump the property set { attrp of described prediction example set P ₁, attrp ₂, attrp _j..., attrp _numpas forecast sample, and train with support vector machine method, obtain nump and predict label, give nump of described prediction example set P affiliated cluster by described nump prediction label difference assignment; Thus the rough sort completed described prediction example set P;

Step 5, to nump prediction example carry out many Tag Estimations;

Step 5.1, be upgrade example set the ψ time by nump exemplary integrated in num example in described initialization example set D and described prediction example set P $D_{new}^{\left(\mathrm{\&psi;}\right)}=\{{\mathrm{inst}}_1,{\mathrm{inst}}_2,...,{\mathrm{inst}}_i,...,{\mathrm{inst}}_{num};{\mathrm{instp}}_1,{\mathrm{instp}}_2,...,{\mathrm{instp}}_j,...,{\mathrm{instp}}_{nump}\},$ Be designated as $D_{new}^{\left(\mathrm{\&psi;}\right)}=\{{\mathrm{inst}}_1^{\left(\mathrm{\&psi;}\right)},{\mathrm{inst}}_2^{\left(\mathrm{\&psi;}\right)},...,{\mathrm{inst}}_{\mathrm{\&Omega;}}^{\left(\mathrm{\&psi;}\right)},...,{\mathrm{inst}}_{num+nump}^{\left(\mathrm{\&psi;}\right)}\};$ represent that Ω upgrades example the ψ time; 1≤Ω≤num+nump;

Step 5.2, described the ψ time renewal example set middle num+nump upgrade in example n attribute of each example respectively as n dimension coordinate, thus obtain Ω the ψ time renewal example example is upgraded the ψ time with ξ the Euclidean distance of attribute 1≤ξ≤num+nump and ξ ≠ Ω;

Step 5.3, formula (9) is utilized to obtain Ω the ψ time renewal example the attribute degree of polymerization thus obtain the attribute degree of polymerization of num+nump the renewal example upgraded for the ψ time

${\mathrm{\&Gamma;}}_{\mathrm{\&Omega;}}^{\left(\mathrm{\&psi;}\right)}=\underset{\mathrm{\&xi;}=1}{\overset{num+nump}{\&Sigma;}}f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})---\left(9\right)$

When $d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}\&le;d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})=1;$ When $d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}>d_c^{\left(\mathrm{\&gamma;}\right)}$ Time, $f(d_{\mathrm{\&Omega;}\mathrm{\&xi;}}^{\left(\mathrm{\&psi;}\right)}-d_c^{\left(\mathrm{\&gamma;}\right)})=0;$

Step 5.4, initialization j=1;

If a jth prediction example instp in the described prediction example set P of step 5.5 _jaffiliated cluster be clup _jwith i-th known example inst in described initialization example set D _iaffiliated cluster be clu _iidentical; Formula (10) is then utilized to obtain i-th known example inst _iexample instp is predicted with jth _jinfluence power gra _ij:

${\mathrm{gra}}_{ij}=\frac{{\mathrm{\&Gamma;}}_i\&times;{\mathrm{\&Gamma;}}_j}{d_{ij}}---\left(10\right)$

In formula (10), Γ _irepresent known example inst _iexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, Γ _jrepresent prediction example instp _jexample set is upgraded at the ψ time the corresponding attribute degree of polymerization upgrading example, d _ijrepresent described i-th known example inst _iexample instp is predicted with jth _jthe Euclidean distance of attribute;

Step 5.6, repetition step 5.5, thus obtain a jth prediction example instp _jwith the influence power of described other known example of initialization example set D, and record maximum effect power gra _max;

If step 5.7 gra _ij=gra _max, then labp is made _j=lab _i, represent the tally set labp of described prediction example set P _jin each label and the tally set lab of described initialization example set D _iin each label identical, thus obtain the prediction example of jth many Tag Estimations;

Step 5.8, by j+1 assignment to j, and judge whether j≤nump sets up, if set up, then return step 5.5 and perform, otherwise, has represented many Tag Estimations nump being predicted to example;

2. self-adaptation many Tag Estimations method according to claim 1, is characterized in that: in described step 5, also comprises step 5.9, by the described the ψ time renewal example set of tally set assignment to described correspondence completing the prediction example set P of many Tag Estimations in, thus obtain ψ+1 renewal example set example set is upgraded+1 time with described ψ the many Tag Estimations of self-adaptation are carried out as new initialization example set.

3. self-adaptation many Tag Estimations method according to claim 1 and 2, it is characterized in that: when occur new there is the prediction example of identical characteristics of objects and identical Object Semanteme time, only need can complete from step 4 and many Tag Estimations are carried out to new prediction example.

4. self-adaptation many Tag Estimations method according to claim 1, is characterized in that in described step 2.9, amendment threshold value rule be: if then will deduct τ ₂assignment is given otherwise, will add τ ₂assignment is given 0.1≤τ ₂≤ 0.5,75%≤τ ₁< 100%.