Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (1): 110-116.doi: 10.11896/jsjkx.181001921

• Database & Big Data & Data Science • Previous Articles     Next Articles

Stacked Support Vector Machine Based on Attacks on Labels of Data Samples

JIN Yao1,XU Li-ya1,LV Hui-lin1,GU Su-hang2,3   

  1. (School of Information Science and Engineering,Changzhou University,Changzhou,Jiangsu 213164,China)1;
    (School of Digital Media,Jiangnan University,Wuxi,Jiangsu 214122,China)2;
    (College of Information Engineering and Technology,Changzhou Vocational Institute of Light Industry,Changzhou,Jiangsu 213164,China)3
  • Received:2018-10-15 Published:2020-01-19
  • About author:JIN Yao,born in 1971,master,associate professor.His main research interests include the computer application technology,and library and information scie-nce;GU Su-hang,born in 1989,doctoral student.His main research interests include artificial intelligence and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (81701793) and Science and Technology Project of Changzhou (CJ20190016).

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Abstract: As for the adversarial data samples which indeed exist in real-world datasets,they can mislead data classifiers into correct predictions which results in poor classification.However,reasonable utilization of the adversarial data samples can distinctly improve the generalization of data classifiers.Since most of existing classifiers do not take the information about adversarial data samples into account to build corresponding classification models,a stacked support vector machine called S-SVM based on attacks on the labels of data samples which aims to obtain outperformed classification performance by learning the adversarial data samples was proposed.In a given dataset,a certain percentage of data samples are randomly chosen as adversarial data samples,in other words,the labels of these chosen data samples are substituted by the other labels included in the given dataset which are different from the original labels of the chosen data samples.Adversarial support vector machine (A-SVM) can be consequently generated by using the support vector machine (SVM) to train the given dataset which contains the adversarial data samples.The first-order gradient information on the output error of the generated A-SVM with respect to the input samples can be then computed,and the input samples will be updated by embedding the first-order gradient information into the original feature space of the input samples.Consequently,the updated data samples can be input into next A-SVM to be trained again to gradually improve the classification performance of the current A-SVM.As a result,S-SVM is formulated by stacking some A-SVMs layer by layer,the best classification results can also be obtained by the corresponding S-SVM.In terms of theoretical analysis and experimental results on UCI and KEEL real-world datasets,the mathematically computed first-order gradient information based on learning the adversarial data samples not only provide a positive relation between the outputs and the inputs of a classifier,but also indeed provide a novel way to stack the front and rear sub-classifiers in the proposed S-SVM.

Key words: Adversarial data samples, Attacks on labels, Stacked structure, Support vector machine (SVM)

CLC Number: 

  • TP391.4
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