TW201126354A - Method for multi-layer classifier - Google Patents

Abstract

The present invention relates to a method for multi-layer classifier applying on a computer readable medium for classifying multiple image samples. The method at least comprising the following steps: (a) receiving a plurality of samples; (b) providing a plurality of attributes, and evaluating a significance of the attributes by a selection criterion; (c) selecting at least one cut-point to establish a discriminant analysis model; (d) proceeding a step of evaluating a performance of the discriminant analysis model by adding the attributes to the discriminant analysis model; and (e) providing a stop criterion. The present invention also provides a computer readable medium for classifying multiple image samples by using the method for multi-layer classifier.

Description

201126354 VI. Description of the Invention: [Technical Field] The present invention relates to a multi-level classification method, and more particularly to a classification method suitable for establishing a multi-layer discriminant analysis model and determining attribute selection and tangent points. [Prior Art] The classification method is very versatile. For example, in the financial industry, when a bank audits a credit card user, the bank can discern whether the applicant will easily become a bad debt; in medical science, the cell organization can be judged as Normal or abnormal; In marketing research, it can be judged whether such marketing methods can attract customers to purchase goods. Therefore, in the field of data mining, the part that is important and important is to explore the classification method. The classification method is a method of superv丨sed丨eaming. The so-called supervised learning method is to enter the target output value and enter ^tf (unsupen.sed leading) ^ ^ ^ ^ # ( Principal ^ ^ ^ ^ Supervised learning method. In the classifier method, it is generally necessary to select the appropriate attribute to establish the classification model. a » ^ ^ θ For example, use body and weight to judge $疋 girl's height And the weight is called the attribute. When the classification model is established, it is often used to build the knife (―ng Samples), and the other group is the two groups. The group is the training sample sample to verify this score::: Two: Class model 201126354 "Yueyue" is more common with two existing classification methods, respectively, which is the common Fisher's linear discriminant analysis in the multivariate statistical analysis.

Analysis, FLD), and classification and regression tree (6) coffee (4) regression trees, CART) 〇Μ ^^ f # Λ ^ , in the classification method, especially in the selection of attributes, some attributes can only identify specific categories and affect their points (4) The accuracy of the classification; and in the establishment of the previous classification model 'will be different due to the choice of its attributes' or the performance of the discriminant analysis model of the desired classification, and thus affect the accuracy of the classification. Therefore, the goal is to create a new multi-level classification method to solve the problem. SUMMARY OF THE INVENTION The main object of the present invention is to provide a multi-level classification method, which uses a multi-layer discriminant analysis model to find one or two tangent points in each layer to classify one or two categories, and each layer You can use multiple attributes at the same time and find the best linear combination of these attributes through Fisher Discriminant Analysis. The present invention provides a multi-level classification method for classifying a plurality of image samples in a computer recordable medium. The computer recordable medium includes a processor, an input device, and a storage device. The method includes at least: a) receiving a plurality of original samples; (b) providing a plurality of attributes, and performing a significant evaluation of the attributes of the original samples with a multivariate parameter; (c) selecting at least all points and establishing a discriminant analysis model , which is one of the significant ones after the evaluation in step (b), by providing a variable homogenous 201126354 ^ analysis parameter to filter out at least the "cut point", and the attributes are evaluated after the explicit person t is included The plurality of original samples are grouped into at least one category to establish the discriminant analysis model, wherein the at least one category includes a first category (NodeA), a third category _eB) 'and an undetermined third category _~) (4) Carrying out the steps of evaluating the performance of the model, which is to add the heart to the discriminant analysis model to perform (four) sexual evaluation; #中,#Add these attributes = improve the discriminant analysis model When you are sexual, you enter the discriminant analysis model - layer 'and then filter at least all the points with the homogeneity analysis parameters'. Adding these attributes to the discriminant analysis model is evaluated by the significant spit. The plurality of original samples continue to be grouped into a first category (NodeA), a second category (N, and an undetermined third category) with an eight (4) plus A condition, and the stop condition is to select the variable homogenous. Knife the parameters, the right does not reject the null hypothesis, (the discriminant analysis model stops the down-layer grouping; or in the step of evaluating the model performance, adding these rents to the tooling method) When these attributes cannot be promoted to the significance of the discriminant analysis model, if the null hypothesis is rejected, the word analysis model stops the next layer grouping. The present invention also provides a computer for classifying multiple image samples. The system classifies the image sample lines by establishing a multi-level classification method. According to the multi-level classification method of the present invention, in the last _ layer classification layer of the _ type Decides that the number of samples included in the second category is zero. In other words, according to the final result of the multi-level classification method of 6 201126354 invention, a plurality of original samples must be classified into the first category (N〇deA) and/or In the second category (NodeB). According to the multi-level classification method of the present invention, the selection of the multi-variable parameters is not limited, and is preferably Wilk's lambda or Gini index; the selection of such attributes is not limited, and preferably at least one selected from the group consisting of ringPDVlmax , VeinCentralVImin, VeinTDCentralVImax, TDVImax, Cl., RMV, CI2, MCI3, and MI2. In addition, there is no limit to the choice of variable homogeneity analysis parameters, preferably Gini index, Mahalanobis distance, or Youden's Index ° Aspect, for the significance evaluation calculation, the p-value calculated by a F statistic 'this p-value indicates the average difference of the attributes among the categories, or the balance is not pure (丨mpurity) criterion judgment; wherein, the F statistic is ρ Λ » This impureness is

Impurity = + NiU x Gini(txl) +Ν,χ Gini{tR) (Nl+Nm+Nr) ; where n is the sample size, p is the number of attributes, and Λ is Wilk's lambda; where '' Nl is the sample space of the first category, Nm is the sample space of the third category 'NR is the sample space of the second category, tL is the Gini value of the first category, tM is the Gini value of the third category, and tR is the second category Gini value. 201126354 According to the multi-level classification method of the present invention, the step of evaluating the performance of the model may include the following four methods: adding these attributes in the same layer as the discriminant analysis model established in step (C) to increase the discriminant analysis The ability to distinguish the original layer in the same layer; add these attributes to the third category (NodeN) and add a layer to create a model. This model also filters out at least everything from the homogeneity analysis parameters. The plurality of original samples determined to continue to be grouped into a first category (NodeA), a second category (N〇deB), and an undetermined third category... "to"; the first category (NodeA) is set to be undecided Category, and add the first attribute (NodeA) plus the undetermined third category (N〇deN) to add a layer to create a model. This model also uses this variable to analyze parameters. Filter out at least all points, and continue to group the remaining undetermined plural original samples into the first category Wodej, the second category Mod#), and the undetermined third category (Nod%); or the second category (N〇deB) is set to an undetermined category, and the combination of the second category (NodeB) plus the undetermined third category heart ^^ is added to the attributes and a layer is added to create a model. The model also screens out at least all points with this variable homogenous knife analysis parameter' to continue to group the remaining undetermined plural original samples into the first category (NodeA), the second category (NodeB), and the undetermined third category. As can be seen from the above, the present invention provides a new discriminant analysis model structure and method which is similar to a tree-like classification structure, which divides data from top to bottom. Unlike the tree structure, this discriminant analysis Each layer of the model will classify some data for _ or two categories, and leave 8 201126354 undetermined data to the next layer. In addition, 'each layer can select some attributes and use Fisher's discriminant analysis to make a linear combination. Lu Zhi, the present invention uses the above method to establish a new multi-layer discriminant analysis model, one layer may only distinguish _ categories (N〇dej NodeB) or both categories can be distinguished (he is like eight and N deB), and leave the sample of the undetermined category to the next __ layer for discriminating. This discriminant model analyzes and includes the method and criteria for selecting the effective variable of each layer in the discriminant model analysis and finding the tangent point, and evaluating The step of model performance considers the overall performance to determine how to construct the model when adding new attributes, and establishes a stop condition to avoid over-adaptation. Therefore, according to the present invention, a method of attribute selection and tangent determination is also provided, and The discriminant analysis model considers the performance of the overall model when adding new attributes to determine how the discriminant analysis model should be established and its stopping conditions. Therefore, the quasi-breaking of the classification is greatly improved. [Embodiment] Figure 2 shows a computer. A schematic diagram of the architecture of the recording medium, which can be used to perform a multi-level classification method of the multi-layer discriminant analysis model of the present invention. As shown in Fig. 2, the computer recordable medium 1 includes a display device 13, a processor 12, a memory u, an input device 14, and a storage device. In the basin, you can use the input image, text, instructions and other information to the computer. The device can be stored in a system such as a hard disk, a CD player or a network connected by the Internet. The data η is used for temporary storage of data or execution: 201126354 Processing benefit 12 is used to calculate and process data, etc. Display device 13 is used to display the output data. The computer recordable media shown in FIG. 2 generally executes various applications such as a word processing program, a drawing program, a scientific computing program, a browsing program, an email program, etc. under the operating system (Operating System). In the present embodiment, the storage device 14 stores a program for causing the computer recordable medium to perform the hierarchical classification method. When the computer recordable medium is to perform this sorting method, the corresponding program is loaded into the memory port to cooperate with the processor 12 to execute the method. Finally, the related data of the classification result is displayed on the display device 13 or stored in a remote database through the Internet. By means of the method of the present invention, the flow diagram can be as shown in Fig. u. According to the multi-layer discriminant analysis model architecture established by the invention, as shown in Fig. 1b, the similarity to the classification tree is that the data is divided from top to bottom. However, unlike the classification tree, the multi-level classification method of this case will judge some or all of the original samples for each layer, and these identified categories (NodeA) or NodeB will not enter. The model of the next layer, leaving only the level determined in this layer as the undetermined category (N〇deN) to add a new attribute to the next layer to discriminate it's and each layer can only be judged - one category or two If you only want to judge a category, you only need to find a point to make the sample (four) into two parts... the part can be classified at this level, and the other part is undecided and must be left to the next level, but if you want to judge two The category needs to find two pointcuts, and cut the data into three parts, one is the first category (NodeA), the other is the second category (N〇deB), and the remaining part is the undetermined third category (Νο&amp ;ν). And each time I want to add a new attribute 201126354, I will consider the performance of the overall model. Let the original layer--the judgment of the layer be combined with the new layer to add a new one to the ai\zhanshan+ or 疋The new attribute claws set 77 dog handlers for those who have not yet been classified. New properties are continually added to this model until the model reaches the stop condition. Hereinafter, the multi-layer j-addition-person classification method of the present invention and the established multi-layer discriminant analysis model architecture will be described in detail. First, accepting a plurality of original samples, for which the plurality of original samples must be selected by a plurality of attributes, and a multivariate parameter is used to perform a significant evaluation of the original samples. The evaluation of this significance depends on the provision-variable homogeneity analysis. (4) Select at least all points. If these attributes are evaluated, they are significant, and then use this point to determine which category the sample in the model belongs to (N〇deA, N〇deB, or να) or to stay in the lower-layer's preferred choice is the most significant after evaluation. From this, it is important to select attributes and determine these cut points. After that, the step of evaluating the model performance of the discriminant analysis model established above is to compare the two models by adding more attributes to the model, which may include adding an attribute to the original discriminant analysis model. And using Fisher linear discriminant analysis (FLD) combined with 'the other is a new layer of model. ί attribute and multivariate parameter selection 1 In the selection of attributes, you can use ringPDVImax, VeinCentra丨VImin,

VeinTDCentralVImax, TDVImax, Cl, RMV, CI2 'MCI3, and MI2; and there are two criteria for selecting multivariable parameters. One is Wilk’s 201126354 lambda, which is common in multivariate statistical methods to determine whether the average between categories is different, and the other is to evaluate the purity of Gini index ° on the classification tree.

Wilk's lambda assumes that there are g categories, p attributes, and '~heart = 1"丨:H0 is not true where 'H〇 hypothesis' H, the alternative hypothesis, is the level (dass) The average value of K.

Wilk's lambda : Λ= |W1 _ 1 |B+W| |I+W'B| =n 1 + A; (1) where w is the intra-group variation matrix B is the inter-group variation matrix I-unit matrix ( Identity matrix) The eigenvalue of ice for W"b is true, and after some conversions, it will obey the F allocation (formula 2) test statistic

F Less = ΛΛ', Claw, =p(gO, ., =LP2(g~O2-4 \P2+(gl)2-5 (Formula 2) The statistic F can be simplified to =s[n-(p-g+ 2)/2] 201126354

/T=^!(hA, F p ') juice,

Wilk's lambda can also be converted to a chi-square allocation Bartlett's χ2 statistic test statistic - -[(« -1) - (P + g) / 2] In Λ - z;^ (Formula 3) When there are few categories, F The statistic will be better than the chi-square statistic. Since multi-layer discriminant analysis is better for two categories, we use F statistic. The present invention can compare the p-values calculated by using the aforementioned F statistic for each attribute. The smaller the p-value is, the more significant the difference between the averages of the attributes is. The p-value of each attribute is selected to be the most Significant attributes. If you want to select a new attribute in the same layer, compare the p value obtained by combining the new attribute with the original attribute, and select the combination with the original attribute? The smallest value can be.

The Gini Index has to search for a better or best attribute and cut point each time it is split, so there is a split criterion to evaluate this attribute and the cut point b. The more common criterion is Gini jncjex. Gini index is a measure of impurity, so Gini丨〇 (the smaller the shape, the better. Each attribute is matched with a corresponding tangent to get its Gini index, so each attribute can search for one of the most Good corresponding cut-point. When making variable selection, just compare the Gini index of each attribute with its corresponding optimal cut point to select the best attribute and cut point here. 201126354 (Equation 4) Suppose now there is g For each category, Gini Index is defined as: >*j Impurity is: ^Gini(tL) + ^-Gini{tR) where is the category/proportion of node tP{j\ t) is the proportion of the category j at the node t~ the number of samples of the left node~ the number of samples of the right node N=~' is the sum of all the samples here. 'The multi-layer discriminant analysis model of the present invention The difference between the classification trees is that the 'classification tree is a binary segmentation at each node, but in the multi-layer discriminant analysis model of the present invention, 'each layer must divide the data into two nodes, so the impureness is The calculation should be changed to

Impurity = NJNs, parent Gini[tiU) + N0Giniq (Nl+Nm +Nr) (Equation 5) The present invention can compare the inefficiency obtained after each attribute with its optimal set-cut point, and select an attribute with the least purity. . If you want to add a new attribute on the same layer, you can use the difference between the new attribute and the original attribute through the FLD combination to calculate the inferiority of 'the lowest purity attribute after combining with the original attribute. . 14 201126354 [Cut Point Selection] There are three methods for selecting the tangent points, namely Gini index, Mahalanobis distance, or Y〇uden, s Index.

Gini Index

When using Gini index to select attributes, each attribute must be selected - group cut points to match to get its impureness, so you need a method selection - the group takes good cut points to match, to get the lowest impurity, if it is in the classification tree In that, just look for a tangent point, so the way to find the tangent point in the classification tree is to try all possible cut points and find a cut point with the lowest purity. However, in the multi-layer discriminant analysis model of the present invention, for example, it is necessary to obtain two tangent points to divide the data into three groups. In this case, it is assumed that there are # samples, and it is only necessary to test W types to find a tangent point. There is a possibility that there will be a cut point. When the number of samples is large, it is very slow to find all the possible tangent points to find two tangent points. Therefore, the present invention solves the problem and develops a fast search for two tangent points. Methods. First of all, like the general classification tree, first search for all the cut points that may find a kilogram with the data knife into two groups with the lowest purity, C. Then, using C σ , cut the = material into N〇de, and N〇de; f. In Node, search for another cut point that can divide N〇dei into two groups with the lowest purity, c: the same, in Node

The middle search also - the -r t K can divide the (10) island into two groups and the cut point with the lowest purity, as shown in Figure 3. 201126354 So we can get two candidate cut points C(i,c,, and c, which can be composed of these three candidate cut points (C.' (d2) and (Cq, C2) three kinds of tangent points combination, compare these three kinds of tangent point combinations After cutting the data into three groups, the purity is selected, and an optimal combination of tangent points is selected. It is preferable to place the samples with high homogeneity on the left and right sides. Therefore, when searching for ^, the limit is set, and the limit is set by q. In the two groups of data that came out, the data that is farther away from C is less pure than the other group. For the same reason, the same restriction should be set when searching for 6. That is, "G, m" (/A/) <G/mU, . If you use this search algorithm, you only need to search about 2N times to find those three candidate cut points, and then compare the three combinations. Mahalanobis distance According to the present invention Another method of tangent point selection is the Mahalanobis distance, which differs from the Euclidean distance in that the Mahalanobis distance considers not only the difference in the center point between the categories, but also the distribution of each category. For example, If there is a sample distance A and B The other centers are the same. 'If the A category has a large number of variations, the distribution is more scattered, and the b category has a smaller variation. The distribution is very concentrated. The sample is farther away from the A category. The Markov distance of the category is small, so it is considered that the comparison belongs to the category A. The method of applying the Mahalanobis distance to the classification is described in detail below. First, it is assumed that there are two categories, and the distance a category can be calculated. The Mahalanobis distance is ··仏(1)=yJ(X -» 5, and the Mahalanobis distance of the distance B category is .dh(x,= buckle-...)rsk'xD where M/Gp/k,.·./, ) is the A category of 16 201126354 The average is the A-category covariance matrix (c〇variance), A=(/WW,) is the average of the B categories, the total number of variants of the category is 0 (7)) < Then belongs to category A, and belongs to category b. In the multi-layer discriminant analysis model of the present invention, the present invention divides a plurality of samples into A, B two categories (N〇deA' NodeB) and undecided (N〇deN) These three groups, so the original q (J〇<Aj(Y), the number of samples belonging to the A category, and then use these originals already The sample broken into the A category is calculated as a new f^A\ λέβ\ L ' A, ' and then the samples that have been judged as the Α category are recalculated with the new mean and the number of variances. The DH (x) )=^-Mm)rs-](x_Mfji) If )U)<~(7), it belongs to category A; and Α|(χ)>ζ^(χ),. is undecided. The same 'take the original &〇〇>& 00 'the number of samples belonging to the B category, and then use these samples that have been judged as 8 categories to calculate a new 仏 2 ' ' 'S«2 ' and then These samples, which have been judged to be in the B category, are recalculated with a new average and the number of mutations, D, 2M = ^MA2yS; A'2 (x~P/l2), Dh2{x) = yli^2ys; \(x-Me2) If %00>仏0〇' belongs to category B, and Z)42(x)<仏w, it is undecided. It should also be noted that when the Mahalanobis distance finding point is used in the multi-layer discriminant analysis model of the present invention, the main purpose is to divide the data into A categories and compare them to the B category, and then use the two data pieces. The collection is to find the pointcut we want 201126354, but when the two sub-collections of data are as shown in Figure 4a, because the choice of its nodes will affect the accuracy of non-classification, in order to improve the situation, the sample size is greatly different. It is unreliable to cause the Markov distance tangent point, and the Gini index can be further used to correct the Mahalanobis distance, as shown in Fig. 4b. First, use the Gini index to find a tangent point, then use the tangent point to divide the data into two sides, and compare the proportion of each category on the two sides. If the proportion of the A category on the left side is larger than the ratio on the right side, then the right side is used. The A category is removed, otherwise the A category of the left material is removed. Similarly, the B category also compares the proportion of the left and right sides, and removes the B category with a smaller proportion. Then use the remaining A categories, B categories to recalculate the average and the variance, and get a Markov distance corrected by Gini index.

Youden's Index First, define the Younde's Index = specificity+sensitivity-1, where specificity is the correct proportion in all A-category samples in the provided original samples, and the sensitivity is all B-categories in the plurality of original samples provided. The correct ratio is judged in the sample, so the higher the Y〇uden's index, the better. The method of searching for the pointcut is similar to using the Gini index. First search for all the possible cut points that divide the highest score of the Younde's Index, C. Then use C. You can cut the data into Node, and Node«. In Node, search for a tangent point 'C' that can divide Node into two groups and the highest Younde’s Index. The same 'in Node,' also search for a cut point that can divide the Node into two groups and the highest point of the Younde's Index, c. So, we can get 201126354 to three candidate cut points C (>, c, , and c, with these three candidate cut points can be composed of (c〇, ς) '(c,, q) and (c., q) three kinds of tangent points combination, compare these three kinds of tangent point combination to cut the data into three groups of Youden ' s Index, select an optimal combination of tangent points. When split into two groups, the calculation of the specificity and sensitivity should be changed because there are undetermined parts.

Specificity=(A category judges the number of samples +0.5* is not discriminated as the number of A categories) the total number of samples in the ZA category;

Sensitivity=(B category judges the number of samples + 〇·5* unrecognized b category sample number) / B total sample number; Then, select the three sets of cut points in the Y0Uden, the highest set of cut points. [Evaluation of Model Effectiveness] In the multi-layer discriminant analysis model, the step of evaluation can be performed in the following four different schemes when adding an attribute into the model each time. First, as shown in Fig. 5, it is assumed that there is a layer of unconformed model and X is used to divide the sample into three groups, namely A category, B category, and undetermined samples, respectively, NodeA, N〇deB, NodeN. To represent. Option 1: Add the attribute < in the original layer, and use FLD combination with A to increase the difference of the original layer. 201126354 Add attribute 1 to Node, . to build a model, and use this model to distinguish samples that are not distinguishable in the original layer. Scheme 3: Combine the NodeA and NodeN samples, and use ν〇<ν to indicate that the model of the original layer A is only used to distinguish the B category, and add the attribute A to the N〇deAN to build a model. This model is used to distinguish between samples that are not distinguishable in the original layer. Scheme 4: Combine the samples of Ν〇<^β with N〇deN, denoted by N〇d^. At this time, the model of the original layer A is only used to distinguish the category A, and is added to N〇deBN. The attribute seven builds a model 'use this model to distinguish the samples that are not distinguishable in the original layer. ί stop condition | In the stop condition of the multi-layer discriminant analysis model of the present invention, it can be divided into two types, namely, to decide whether to continue to divide the undetermined sample, and to decide whether to add a new layer to the existing layer. Attributes. In deciding whether to continue to divide the undecided sample into the next division, you can use the position mentioned in the attribute selection, s !, if you do not reject the null hypothesis, the representative will not be able to find it in the remaining samples. Make a significant area between categories as an open attribute, so stop continuing to split down. As mentioned above, another stop condition is to decide whether to add new attributes to the original layer. Since the model already exists - some significant green, if you want to add new attributes, it is not a new addition. After the stratification of the overall model 20 201126354 is not significant enough 'it is to consider how much variation is additionally explained by the newly added attributes. Here, reference can be made to the partial F-test used in the forward selection method in the regression analysis, which is to verify that there is no significant difference between the model in which a new attribute is added and the original model. If the null hypothesis is rejected, there is no significant improvement in the model for adding new attributes, and this attribute is not added to the model. The verification model is (Equation 6) ί^〇' y = β〇+ β\^\+ (full model) = β〇+ β'χ\ (reduce model) f ^ ^.SR^Xi>X2)~ SSRjX ^) ^ SSE{Xl,ΧΊ) SSR{X]) χΊ) SSE(X"X,) H do 1 dfn~dfF dfF 6) where, also the degree of freedom of the flill model; the degree of freedom of the reduce model; ;厶〇,/5 i,A is a parameter of the variable; the human SSR is the human-interpreted average (eXpiaine(j sum SqUare) ·, and the SSE is the human remaining sum (e residuai sum SqUare). The forward selection method of discriminant analysis, whose model is (Equation 7), if the null hypothesis is rejected, indicates that the model does not need to be added to this new attribute. (full model) (reduce model) (Equation 7) //〇.d = ύ) χΧλ + 〇)2Χί Η{ . y = 〇)]Χ] w2) If the new attribute added is significant enough, compare the performance of the model before and after the addition with the evaluation model performance. Conversely, if you add a new one If the performance of the overall model cannot be improved after the 201126354 attribute, the new attribute is stopped. It should be noted that the multi-level classification method of the present invention and the established multi-layer discriminant analysis model architecture are in the model. The last layer is forced to classify all the data. 'The undetermined samples can no longer be left. According to the above parameters and setting conditions, the detailed flow chart of the multi-layer discriminant analysis model based on the method of the present invention is shown in Fig. 6. First, in the After accepting a plurality of original samples (not shown), use wuk, s lambda or Ginnndex to select a most significant attribute, and then check whether the attribute has a significant difference in the ability of each category. If the null hypothesis is rejected, it represents This attribute has the ability to interpret. Reuse the Markov distance or Gini index as described above to find the best set of cut points for this attribute, and divide the data into the first category (A category, NodeA), the second category (B category, N 〇deB) and the undetermined third category (N〇deN) three groups of data, and then you can estimate the performance of the model based on the three groups of data. Next, when selecting the second attribute, consider Where is the second attribute added? The four options mentioned above are: (The plan says the original layer, find the best attribute after combining with the original variable. The cut point '(Scheme 2) uses the original undetermined group of samples to find the most suitable attribute and the cut point] Scheme 3) The A category is considered undecided, and the A category plus undecided

The sample is used to find the most suitable attribute and tangent point; and (Scheme 4) treat the B category as pending $' with _ plus undecided samples to find the most suitable attribute and cut point. “Each plan should use suspend after selecting the eyebrow, (3) secretly determine its explicitness. If you think that it is not significant enough, discard this attribute, and then use the above-mentioned method to estimate the model effect to evaluate each (4) (4). The effectiveness of the model. If the performance of the scheme is the best, add the new attribute to the original layer. If the performance of the scheme 2 is the best, use the remaining undetermined samples of the previous layer to create a new layer model. For scenario 3 or scenario 4, it is best to judge the a category by considering the eight categories «category as undecided and creating a new layer model with all remaining undetermined samples and converting the upper model into only one tangent point. Or B category, not in the same layer to judge two categories. If the current model already has n layers, to add a new attribute, add the new attribute to the original layer, this program will have η cases 'plus the program 2, 3, 4, a total of η + 3 cases should be considered. If the new variables in the η + 3 cases are not significant, the model will stop. If there is a plan to pass, select the best performance plan, k check this The scheme is selected after the selection of an attribute. If there is no improvement, the model will stop adding new variables. If there is improvement, continue to add new attributes to the model, and continue to add variables until the model performance is no longer improved. The discriminant analysis model provides a systematic variable selection method. You can use wiUc's |ambda to convert to the F-valued p-value or Gini index to select the variable. "In the decision of the tangent point, the Markov distance is also provided." Gini index and other methods. When using Gini index to determine the tangent point, since it is necessary to find at least all the points, if searching for all possible tangent point combinations will be very time consuming, the present invention also provides a method for searching for the desired cut point more quickly. When using Markov distance to determine this at least all points, we will first use the Mahalanobis distance to divide all samples into A-category and biased b-types, and then use these two groups of samples to find two Markov distance tangent points, but because of Using 201126354 Mahalanobis distance to divide the data into two groups, the sample size gap between the two groups is usually very large, and the sample size gap between the categories will be It is unreliable to find the tangent point in the Markov distance. Therefore, the present invention provides a solution to correct this problem by using Giniindex to correct the Markov distance. Whenever a new attribute is added to the model, not only the performance of one layer is considered, but After considering the performance of the overall model, it is decided to add new attributes. In the stop condition of the model, it also provides the use of such as Wilk's 丨ambda to prevent over-adaptation of the model, thus greatly improving the accuracy of the classification. [Embodiment η In the present embodiment, a data of a sample number of 100, 2 categories, and 5 attributes (Χι, Χ2, ..., X5) is provided, each of which is subject to Ν(〇, ι). The category scatter diagram is shown in Figure 7b' and its default model is shown in Figure 7a. "The first layer will use 乂 to explain 'the part that cannot be classified and then leave it to the next layer to explain X2" The results obtained by multi-layer discriminant analysis are shown in Fig. 7c. Since the multi-layer discriminant analysis model uses both the Gini index and the Mahalanobis distance to find the tangent point, the results of the multi-layer discriminant analysis will be put on both methods.As for the results obtained via CART, it is as shown in Figure 7d. You can compare the multi-level discriminant analysis using Cini index to find the tangent point and CART. The two criteria for finding the tangent point are the same. In the multi-layer discriminant analysis, the first layer uses X to classify categories and categories. 1 'Category 0 contains 24 categories and 0 categories 1. Category 1 contains 3 categories 0 and 35 categories 1. However, as shown in Figure 7d, in CART, X is used in the first layer, category 1 is included, and 3 categories 0 and 35 categories 1 are included. On the 24th 201126354 layer, X is used again, and 1 x is used. - Six different 1 'contains 24 categories 0 and 0 categories 1. The results of the 蚌 ft and knives are the same. However, the multi-level discriminant analysis At曰 discriminates this attribute into two categories (category 0 and category 1) in the layer, and the force is used, but in CAR1^£, only one category can be discriminated in the first layer. One genus + student discriminates another category. The results of the eight sets are shown in Table 1. As can be seen from Table 1, the results of the multi-level discriminant analysis using Gini index are as good as those of Cart. Multi-layer FLD cut point: Gini index Multi-layer FLD cut point: MD CART FLD Accuracy rate 0.89 0.85 0.89 0.83 Table 1

[Embodiment 2J In the present embodiment, a data of 2 〇〇, 2 categories, 1 属性 attributes (X, X2, ..., χ, 〇) is provided, wherein each attribute is subject to N ( Hey, l). The preset model is shown in the figure. Among them, the first layer will be selected into the \丨2 group to form an FLD model, and those that cannot be classified in the first layer will be explained in the second layer by the FLD model of the combination of 乂3 and 乂4. The results obtained by discriminant analysis by Fig. 8b are shown in Fig. 8b, and the results obtained by cart are shown in Fig. 8c. The results of the method according to the present embodiment are presented in Table 2. This multi-level discriminant analysis is better than CART and FLD, regardless of whether the Gini index or the Mahalanobis distance is used to find the cut point. 25 201126354 ^--- Accuracy rate FLD cut point: Gini index ------ Multi-layer FLD Cut point: MD CART FLD 0.9 0.885 0.83 0.88 Table 2 [Example 31 A sample number is provided in this example 1000, 2 categories, 5 genus···, χ5) The data 'each of them is subject to N(0,1), and the eight types of other layouts are shown in Figure 9b. The default model is shown in Figure Shown in 9a. The first layer will use X to explain 'and Χ| only the ability to classify the category ,, and the remaining unclassifiable parts will be left to the next layer to explain & The result obtained by the discriminant analysis by the eve layer is shown in Fig. 9c, which is obtained by CART. If it is #, as shown in Figure 9d. Since the preset model can be regarded as a univariate tree, structure, in this case, the multi-level discriminant analysis uses (5) index as the result of the tangent point will be the same as the result obtained by CART. The results of the method according to the present embodiment are presented in Table 3. The results obtained by the multi-level discriminant analysis using the Gini index are good as those of the cart. Multi-layer FLD cut point: Gini index Multi-layer FLD Cut point: MD CART FLD Accuracy 0.84 0.835 I — 0.84 1 0.785 Table 3

I Embodiment 4J In the present embodiment, the number of data pen samples of one attribute (x, x2, ..., x5) is 丨〇〇〇, two categories, 5, each of which is subject to N ( 0,1). 26 201126354 The preset model is shown in Figure 0a. The first layer will use X to explain the ability to classify category 0. The rest cannot be divided and X, only x2 and χ3 are explained. h then left to the next layer for 'CART. The results obtained through multi-layer discriminant analysis are shown in Fig. 0b. The results are shown in Fig. 10c. The results of the method according to the present embodiment are shown in Table 4. The results obtained by the Gini index are the best. Multi-layer discriminant analysis uses the FLD cut point of the township layer: Gini index Multi-layer FLD Cut point: MD ~----- CART FLD Accuracy rate 0.S65 0.795 0.85 0.79

[Embodiment 5] In the present embodiment, the quantized attributes of some tumor images are obtained by ultrasonic scanning, and then a discriminant model is constructed through these attributes, wherein there are 160 'with 108 categories of tumor image samples〇 Representatives, 52 are represented by category 1. Firstly, the five attributes of CI, έΐ, MI, HI, and ringPDVImax are provided for analysis. If the five attributes are directly combined using Fisher's discriminant analysis, the accuracy rate is 0.793, and the accuracy of using multi-level discriminant analysis is 0.8. In addition, multi-layer discriminant analysis uses only four of these variables, as shown in Figure 11a, and the accuracy is higher than the traditional Fisher discriminant analysis. In addition to the above five attributes, other attributes can be added together for analysis according to this embodiment. The results obtained by multi-level discriminant analysis using Gini index to determine the tangent point are shown in Fig. 27 201126354 1 1 b, and the quasi-reduction rate is 0.906. Multilayer discriminant analysis using Youden's index to determine the tangent results is shown in Figure 1 c, with an accuracy of 0.801 2 . The results obtained by CART are shown in lid, and the accuracy rate is 0.868. FLD uses the nine attributes of ringPDVImax, VeinCentralVImin, VeinTDCentralVImax, TDVImax, Cl, RMV, CI2, MCI3, and MI2, and the accuracy is 0.843. As shown in Table 5, the multi-layer discriminant analysis yields the best accuracy. Multi-layer FLD cut point: Gin i index Multi-layer FLD cut point: Youden's index CART FLD Accuracy rate 0.906 0.801 0.868 0.838 Table 5 Furthermore, the above-mentioned execution steps of the present invention can be written in a computer language for execution, and the software program written in this can be stored in any micro The recording medium that the processing unit can recognize and interpret, or the items and devices that contain the recording medium. It is not limited to any form, and the article can be a hard disk, a floppy disk, a compact disc, a ZIP, an MO, a 1C chip, a random access memory (RAM), or any other person familiar with the art. Media items. Since the multi-level classification method of the present invention has been disclosed as before, anyone who is familiar with the computer language knows how to write a software program after reading the present specification, and thus the details of the software program are not described here. The above-described embodiments are merely examples for the convenience of the description, and the scope of the claims is intended to be limited by the scope of the claims. 28 201126354 [Simplified description of the drawings] Fig. 1a is a flow chart of multi-discrimination analysis of the present invention. Architecture Figure 1b is a schematic diagram of a multi-layer discriminant analysis model established in accordance with the method of the present invention. Figure 2 shows a schematic diagram of the architecture of a computer recordable medium. 3 is a schematic diagram of a search (3) port cut point according to a preferred embodiment of the present invention. Figures 4a-4b are schematic views of the preferred embodiment of the invention used to modify the Mahalanobis distance. Figure 5 is a schematic illustration of four modes of a comparative model of the present invention. Figure 6 is a detailed flow chart of the multi-layer discriminant analysis model of the present invention. 7a-7d are schematic views of Embodiment 1 of the present invention. 8a-8c are schematic views of Embodiment 2 of the present invention. 9a-9d are schematic views of Embodiment 3 of the present invention. 1a-l〇c are schematic views of Embodiment 4 of the present invention. Figure 11 - 11 d is a schematic view of Embodiment 5 of the present invention. [Description of main component symbols] Computer recordable media 1 Memory 11 Processor 12 Display device 13 Input device 14 Storage device 15 29

Claims (1)

201126354 VII. Scope of application for patents: 1. A multi-level classification method for using two types of multiple image samples in a computer recordable medium. The computer recordable medium includes a processor, an input device, and a storage device. The method comprises at least the following steps: (a) receiving a plurality of original samples: = providing a plurality of attributes, and performing a significant evaluation of the original samples by the multi-variable parameters; the bolus is at least - the tangent point And establishing a sentence-analysis model, which is one of the significant ones after the ==, by providing a-variable with the at least-cut point, and evaluating the attributes to have the plural originals included in the towel The purely sub-group is at least one category discriminant analysis model, wherein the at least-category includes the second-two-two? Wide category, and undetermined third category (9). For example, the step of the efficiency of the °f (four) type of performance is to make a significant evaluation of the discriminant analysis model == some attributes; wherein, when the significance of the at least analysis=ΓΓΓ model is added, the judgment point is entered. I, then use the variable homogeneity analysis parameters to filter out to the significant = in the analysis model, add the attributes to evaluate the difference: 1: The plurality of original samples continue to be grouped into the first category and the category (N〇deB), and The third category of decision; analysis Γ) breaks into a stop condition 'The stop condition is to select the variable homogeneity... the right does not reject the null hypothesis'. The discriminant analysis model stops at 30 201126354 or joins in the step of evaluating the performance of the model. These attributes The next layer is grouped; or if the model is analyzed by a regression analysis, the next layer is stopped. 2. The multi-level classification method according to item 1 of the scope of the patent application, wherein the stop condition is added to the last layer classification layer of the discriminant analysis model. The number of samples included in the third category (NodeN) of the unresolved 为 is 3. The multi-level classification method described in item 1 of the patent application scope. The Haixi variable parameter is Wilk, s lambda or Gini index. 4. If the multi-level classification method described in item 1 of the patent application is applied, is the calculation of the significance evaluation calculated by a F statistic? Value to that? Values indicate the significance of the differences between the categories of the attributes; or judged by a measure of impureness; where the F statistic is The impureness is Impurity = x+^A/^Gini{tM) + NRxGini{tR) (Nl + Nm+Nr); where 'n is the sample size, p is the number of attributes, Λ For Wilk's lambda; 201126354 where nl is the sample space of the first category, the sample space of the third category is 'NR is the sample space of the second category, t[ is the value of the first category, and tM is the Gin of the third category • Depreciation, tR is the depreciation of the second category. 5. The multi-level classification method according to claim 1, wherein at least one of the four attributes is selected from the group consisting of ringPDVImax, VeinCentralvlm丨η, VeniTDCentralVImax, TDVI_, ci, RMV 'CI2, MCI3, and ΜΙ2. Group. 6. The multi-level classification method described in item 1 of the patent application, wherein the '4 variable homogeneity analysis parameter is Gini index, Mahalan〇bis distance, or Youden's Index c 7. As described in the scope of the patent application The hierarchical classification method, wherein the step of evaluating the performance of the model comprises: adding the attributes in the same layer as the analysis model established in step (c) to increase the distinguishing ability in the original layer of the discriminant analysis model . 8. The multi-level classification method according to claim 1, wherein the step of evaluating the performance of the model comprises: adding the attributes to the third category Wodh) and adding a layer to establish a model, the model At least all the points are also selected by the variable homogeneity analysis parameter, and the remaining undetermined plurality of original samples are further grouped into a first category (9) to be, a second category ... to 卽), and an undetermined third category ( M〇deN). 9. The multi-level classification method as described in the scope of the patent application scope, wherein the step of evaluating the performance of the model includes: setting the first category to an undetermined category, and adding the first category ^^ Adding at least one attribute to the combination formed by the undetermined third category (NodeN) and adding a layer to build 32 201126354 to change the type of 'the model also uses the variable homogeneity analysis parameters to filter out at least - the cut point 'will remain The plurality of original samples determined to continue to be grouped into the first category (NodeA), the first category is still cjeij)' and the undetermined third category (7) is ~). 1 0 · as claimed in claim 1 The multi-level classification method, wherein the step of evaluating the performance of the model comprises: setting the second category Modh) to an undetermined category, and adding the second category Mod#) to the undetermined third category (NodeN). Adding these attributes to the combination and adding a layer to establish a model, the model also filters out at least all points with the variable homogeneity analysis parameters, and continues to group the plurality of original samples that are not determined by the stabbing into the first category (NodeA). , the second category (10) as in), and the undetermined third category (then ~). j] · The multi-level classification method as described in the scope of the patent application, wherein the 'edge regression analysis method includes - forward selection method The use of two (four) she, 丨2, a computer recordable medium for classifying a plurality of image samples, the multi-level classification method for classifying the image samples, the recorded medium includes a processor, an input device, And a storage device's hierarchical classification method includes at least the following steps: (a) receiving a plurality of original samples; the Lb-sex, single-multivariate parameters (4) the original samples of the two genera I·sheng for explicit evaluation calculation; (c) select at least all points and have significant significance after evaluation in step (8). 1 = 4 ^ 'The system analyzes the parameters of the qualitative analysis by “producing the error—the variable is the same as the significant one. The plurality of original sample groups included in the evaluation are 201126354 to establish the discriminant analysis model, wherein the at least-category includes a first category (NodeA), a second category (NodeB), and an undetermined number The three categories (ν〇 &ν); (d) the step of performing - evaluating the performance of the model, which is to add the attributes to the discriminant analysis: a significant evaluation; wherein, when the attributes are added, the When discriminating the significance of the analysis model, it enters the lower layer of the discriminant analysis model, and then selects at least 1 tangent point by the homogeneity analysis parameter of the variable, and adds the attribute to the discriminant analysis model to be included in the evaluation. The plurality of original samples continue to be grouped into a first category (N〇deA), a second category (NodeB), and an undetermined third category, and (e) a stop condition is added, the stop condition is selected to select the variable homogeneity Analyze the parameters, if the null hypothesis is not rejected, the discriminant analysis model stops the down-layer grouping; or add the attributes in the step of evaluating the performance of the model to perform the significance evaluation by the regression analysis method, after adding the attributes When the significance of the discriminant analysis model cannot be improved, if the null hypothesis is rejected, the discriminant analysis model stops the next layer grouping. 13. The computer recordable medium according to claim 2, wherein, in the last layer classification layer of the discriminant analysis model, the undetermined third category Tao in the classification condition The number of samples included is zero.叼 1 4. For example, the computer-recordable media mentioned in the 2nd paragraph of the application scope is the Wi丨k, s lambda or Gini index. 1 5. The computer-recordable medium as described in item 2 of the patent application scope, the significance evaluation calculation is that the p-value calculated by the -F statistic is represented by the p-value. 34 201126354 Some attributes are averaged between the categories The difference is significant; or judged by a measure of impureness; where the F statistic is np-\ P, the impureness is Impurity = P_l 乂 Gini, t匕, ····NM Gini , t+ NR xGini, tR) - (Nl+Nm+Nr) . where 'n is the sample space (sainple size), p is the number of attributes, Λ is Wilk's lambda; where NL is the sample space of the first category, For the sample space of the third category, nr is the sample space of the second category, tL is the Gini value of the first category 'tM is the Gini value of the third category, and tR is the Gin丨 value of the second category. 16. The computer recordable medium of claim 12, wherein the at least one attribute is selected from the group consisting of ringPDVImax, VeinCemraMmin, VeinTDCentralVImax, TDVImax, Cl, RMV, CI2, MCI3, and MI2. 17. The computer recordable media mentioned in claim 12 of the patent application. The homology analysis parameter of the Hi. history is Gini index, Mahalanobis distance, or Youden s Index. 18. The computer recordable medium of claim 12, wherein the step of evaluating the performance of the model comprises: adding the attributes in the same layer as the discriminant analysis model established in step (c) to increase The discriminating ability of the discriminant analysis model in the original layer 201126354. The computer recordable medium according to claim 12, wherein the step of evaluating the performance of the model comprises: adding the third category (10) (four) Attributes and a new layer to create a model, the model also filters out at least all points with the variable homogeneity analysis parameters, and continues to group the remaining undetermined plural original samples into the first -_(10)~) and the second type of eggs (four) And the third category that has not been decided (N〇deN). 20. The computer recordable medium of claim 2, wherein the step of evaluating the performance of the model comprises: setting the first category (10)~) to an undetermined category, and the first category (Nodh) Adding these attributes to the combination formed by the undetermined third category (Nodew) and adding a layer to create a model, the model also filters out at least all points with the variable homogeneity analysis parameters, and the remaining undetermined A plurality of original samples continue to be grouped into a first category (N〇deA), a second category ~ as), and an undetermined third category (10), such as). 21. The computer recordable medium of claim 12, wherein the step of evaluating the performance of the model comprises: setting the second category (10) as an undetermined category, and the second category (N〇deB) Adding these attributes to the combination formed by the undetermined third category (N〇deN) and adding a layer to create a model, the model also filters out at least all points with the variable homogeneity analysis parameters, leaving the remaining The plurality of original samples determined to continue to be grouped into the first category (eA), the second category (eB), and the undetermined third category (four). 22. The computer recordable medium as described in claim 12, wherein the regression analysis method includes a _ialFtest used by the forward selection method. 36