CN107133867A - Credit method for anti-counterfeit based on SVMs - Google Patents
Credit method for anti-counterfeit based on SVMs Download PDFInfo
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Abstract
The present invention provides a kind of credit method for anti-counterfeit based on SVMs.The credit method for anti-counterfeit based on SVMs is in the training stage, the sorting technique and homing method of SVMs are organically combined using new object function, and then ensure the more doubtful credits swindle samples of segregation section inside covering of study, and segregation section both sides are respectively normal sample and credit swindle sample；In forecast period, if sample to be predicted falls inside segregation section, predict that the example is swindled for doubtful credit；If on segregation section both sides, according to its fall particular location, it is the swindle of normal or credit to predict it.Compared with correlation technique, the credit method for anti-counterfeit based on SVMs that the present invention is provided can effectively analyze whether sample belongs to doubtful credit swindle, and can keep high-accuracy on and credit swindle sample normal in prediction.
Description
Technical field
The present invention relates to data analysis technique field, more particularly to a kind of credit method for anti-counterfeit based on SVMs.
Background technology
In practice, loan fraud case occurs repeatedly, and huge loss is caused to bank, and loan swindle comes from bank mostly
Bad lending, occur loan swindle be bank's non-performing loan ultimate performance, and it is more the problem of be commercial banking
Embody a concentrated reflection of poor for loan quality, bad-loan ratio remains high.At present, this ratio mean height of Ji great nationalized banks is up to two
Digit, has had a strong impact on the normal operation of business bank, and heavy losses are caused to bank and people's property.
As continuing to develop for China market economy and deepening continuously for market economic system, especially economic structure turn
The formation of rail, the expansion of socio-economic activity, and modern finance system, required specification is inevitable with a large amount of during setting up
Phenomenon of being out of normal activity, loan fraudulent act a large amount of appearance be exactly a more typical example, loan swindle crime very disruptive
Normal financial order, while also destroy the social credit mechanism on one of socialist market economy basis.
Credit swindle classification problem can regard the classification problem of three classes as, i.e., normal, credit swindle and doubtful letter
With swindle.Compared to normal and credit fraud, dubiety fraud is often more worth research, because, this part of credit
Problem is likely to become real credit fraud.
For such issues that, traditional multi-class support vector machine is come pair using one-to-one, a pair other and DAGSVM methods
Multi-class problem is classified.Wherein, one-to-one and a pair of other methods extensive errors are unbounded.Also, it is one-to-one and one
The quantity of sub-classifier to being constructed needed for other methods increases on classification number k into superlinearity, and common k (k-1) is individual, in test rank
Section, it is necessary to calculate all sub- discriminant functions and predict the outcome, then obtains final classification result using voting method.This method
It is not directly perceived, do not utilize understanding of the expert to problem.In addition, each sub-classifier must carry out specification to data in One-against-one
Change, this causes also one most obvious shortcoming to be exactly that each sub-classifier will must be adjusted carefully very much, if some
Sub-classifier lack of standardizationization, then whole categorizing system will tend to study.DAGSVM methods solve inseparable regional issue, and
And be not necessarily intended to calculate all subclassification decision functions, but position of each sub-classifier in directed acyclic graph also can be to dividing
Class system produces large effect.Because credit swindles the particularity of data, the model that these methods are built is not easy to reason
Solve and performance is not ideal enough, be not suitable for the classification that credit swindles sample.Therefore design is needed to be directed to credit swindle data set
Sorting technique.
The content of the invention
The present invention builds new supporting vector machine model to solve prior art problem, design it is a kind of based on support to
The credit method for anti-counterfeit of amount machine, while this method ensures effectively to predict doubtful credit swindle sample, predict exactly it is normal and
Credit swindles sample.
The present invention provides a kind of credit method for anti-counterfeit based on SVMs, including：
Step 1, build one's credit loan variable information table is described；Training sample is obtained, table pair is described according to described information
Training sample is handled, and sets up training samples information table；
Step 2, object function is set up, and utilizes described object function by support vector cassification strategy and supporting vector
Machine returns strategy and merged, and described object function is：
Wherein：
yi(w·xj)≥1-ξj, j=l+1, L, l+m+n,
(w·xi)-yi≤1+ξi, i=1, L, l,
ξj>=0, j=l+1, L, l+m+n,
Wherein, w is the parameter of hyperplane, and l is the number of doubtful credit swindle sample in training sample table, and m and n are respectively
The number of credit swindle sample and normal sample in training sample table, power of punishment when C1 is doubtful credit swindle mistake classification
Weight, punishment weight when C2 is credit swindle sample or the classification of normal sample mistake；
Step 3, parameter C1 and C2 value is determined using 10 folding cross validations：It is 10 that stochastic averagina, which divides training dataset,
Folding, other to roll over for training pattern for each folding, the folding is used for the performance for testing the model trained；Average each folding
As a result the final excellent Generalization Capability of model is obtained；It is respectively [0.1,0.2,0.3 ..., 1.0] to set C1 and C2 spans；It is right
In with the corresponding C1 and C2 values of optimal Generalization Capability model,
Step 4, setOn overall training set, final mask, profit are trained using iterative method
The parameter w solved with object function；Iterative process with for：
Wherein w (k) is kth time iteration w value,It is gradient of the object function at w (k) places, Hk represents kth time
The Hesse matrixes of iterative target function, its initial value H0 is unit matrix, and Hk is calculated using following alternative manner：
Wherein p(k)=w(k+1)-w(k),
Step 5, list processing forecast sample x ' is described using the information of step 1, calculates the output function y=of SVMs
Wx '+b, so the class that is associated with forecast sample x ' of model prediction marked as：
It is preferred that, in step 1, described information describes table and fiduciary loan type of variables is divided into classification, numerical value, two
It is first or discrete.
It is preferred that, in steps of 5, described forecast sample x ' is available：
Substituted into after non-linearization the output function of SVMs.
It is preferred that, the missing attribute of the sample in forecast sample x ' in the training sample and step 5 that are obtained in step 1
Value, is filled after being averaged using other samples are known with property value.
Compared with correlation technique, the credit method for anti-counterfeit based on SVMs that the present invention is provided can be analyzed effectively
Whether sample belongs to doubtful credit swindle, and can keep high-accuracy on and credit swindle sample normal in prediction.
Brief description of the drawings
Fig. 1 swindles sample linear classification schematic diagram for the credit method for anti-counterfeit based on SVMs that the present invention is provided；
The swindle sample Nonlinear Classification signal of the credit method for anti-counterfeit based on SVMs that Fig. 2 provides for the present invention
Figure.
Embodiment
Describe the present invention in detail below with reference to accompanying drawing and in conjunction with the embodiments.
A kind of credit method for anti-counterfeit based on SVMs, including：
Step 1, build one's credit loan variable information table is described；Training sample is obtained, table pair is described according to described information
Training sample is handled, and sets up training samples information table.
Information describes table and is shown in Table 1.Training samples information table is shown in Table 2.
Table 1：Information describes table
In table 1, build one's credit loan variable information table is described, by fiduciary loan type of variables be divided into classification, numerical value,
It is binary, discrete.
Table 2：Training samples information table
In table 2, fetch according to first 4 of concentration as the training sample x obtained, table is described to training according to described information
Sample x processing.The missing attribute values of sample in training sample, average using known to other samples with property value
After fill, the 7th row Salary value in such as table 2, data item the 2nd be its 1st, 3, the average value of the Salary of 4 value.
Step 2, object function is set up, and is returned support vector cassification and SVMs using described object function
Return merging, described object function is：
Wherein：
yi(w·xj)≥1-ξj, j=l+1, L, l+m+n,
(w·xi)-yi≤1+ξi, i=1, L, l,
ξj>=0, j=l+1, L, l+m+n,
Wherein, w is the parameter of hyperplane, and l is the number of doubtful credit swindle sample in training sample table, and m and n are respectively
The number of credit swindle sample and normal sample in training sample table, power of punishment when C1 is doubtful credit swindle mistake classification
Weight, punishment weight when C2 is credit swindle sample or the classification of normal sample mistake.
Step 3, parameter C1 and C2 value is determined using 10 folding cross validations：It is 10 that stochastic averagina, which divides training dataset,
Folding, other to roll over for training pattern for each folding, the folding is used for the performance for testing the model trained；Average each folding
As a result the final excellent Generalization Capability of model is obtained；It is respectively [0.1,0.2,0.3 ..., 1.0] to set C1 and C2 spans；It is right
In with the corresponding C1 and C2 values of optimal Generalization Capability model,
Step 4, setOn overall training set, final mask, profit are trained using iterative method
The parameter w solved with object function；Iterative process with for：
Wherein w (k) is kth time iteration w value,It is gradient of the object function at w (k) places, Hk represents kth time
The Hesse matrixes of iterative target function, its initial value H0 is unit matrix, and Hk is calculated using following alternative manner：
Wherein p(k)=w(k+1)-w(k),
Step 5, list processing forecast sample x ' is described using the information of step 1, calculates the output function y=of SVMs
Wx '+b, so the class that is associated with forecast sample x ' of model prediction marked as：
Train a supporting vector machine model so that normal sample falls in the side of segregation section, as shown in Fig. 1 "+"；Credit
Swindle sample falls another survey in segregation section, as shown in Fig. 1 "○"；And doubtful credit swindle sample falls in the inside of segregation section,
Such as Fig. 1It is shown.
The class of normal, doubtful and credit swindle sample is mapped respectively marked as+1,0 and -1.Operate, standardize for convenience
Supporting vector to hyperplane (dotted line in Fig. 1) distance be 1.
In addition, the forecast sample x ' in step 5 is available：
Substituted into after non-linearization
The output function of SVMs.Obtain result as shown in Figure 2：Normally, the differentiation degree of doubtful fraud, fraud is than linear point
Class is more accurate.
The missing attribute values of the sample in forecast sample x ' in step 5, can utilize same property value known to other samples
Filled after averaging.
Experimental result：Commented, used a pair as evaluation index using accuracy rate (accuracy) and recall rate (recall)
One SVMs is used as comparison other.Accuracy rate is defined as the ratio correctly classified, and recall rate is defined as doubtful credit swindle
The ratio correctly classified in sample.It the results are shown in Table shown in 3.
Table 3：Experimental result table
In table 3, the output function SVM-3C-Linear of linear SVM and the output letter of Nonlinear Support Vector Machines
Number SVM-3C-NonLinear are to support linear and non-linear method, SVM-Linear and SVM- in the inventive method respectively
NonLinear represents man-to-man linear SVM and Nonlinear Support Vector Machines respectively.
Embodiments of the invention are the foregoing is only, are not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (4)
1. a kind of credit method for anti-counterfeit based on SVMs, it is characterised in that including：
Step 1, build one's credit loan variable information table is described；Training sample is obtained, table is described to training according to described information
Sample is handled, and sets up training samples information table；
Step 2, object function is set up, and is returned support vector cassification strategy and SVMs using described object function
Return tactful merging, described object function is：
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Wherein, w is the parameter of hyperplane, and l is the number of doubtful credit swindle sample in training sample table, and m and n are training respectively
The number of credit swindle sample and normal sample in sample table, punishment weight when C1 is doubtful credit swindle mistake classification, C2
Punishment weight when being credit swindle sample or the classification of normal sample mistake；
Step 3, parameter C1 and C2 value is determined using 10 folding cross validations：It is 10 foldings that stochastic averagina, which divides training dataset,
Other to roll over for training pattern for each folding, the folding is used for the performance for testing the model trained；The result of average each folding
Obtain the final excellent Generalization Capability of model；It is respectively [0.1,0.2,0.3 ..., 1.0] to set C1 and C2 spans；For tool
There are the corresponding C1 and C2 values of optimal Generalization Capability model,
Step 4, setOn overall training set, final mask is trained using iterative method, mesh is utilized
The parameter w that scalar functions are solved；Iterative process with for：
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The Hesse matrixes of object function, its initial value H0 is unit matrix, and Hk is calculated using following alternative manner：
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Step 5, list processing forecast sample x ' described using the information of step 1, calculate the output function y=wx ' of SVMs+
B, so the class that is associated with forecast sample x ' of model prediction marked as：
2. the credit method for anti-counterfeit according to claim 1 based on SVMs, it is characterised in that in step 1, institute
The information stated describes table and fiduciary loan type of variables is divided into classification, numerical value, binary or discrete.
3. the credit method for anti-counterfeit according to claim 1 based on SVMs, it is characterised in that in steps of 5, institute
The forecast sample x ' stated is available：
Substituted into after non-linearization the output function of SVMs.
4. the credit method for anti-counterfeit according to claim 1 based on SVMs, it is characterised in that obtained in step 1
Training sample and step 5 in forecast sample x ' in sample missing attribute values, can be belonged to together using known to other samples
Property value average after fill.
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