一种新的代价敏感SVDD二类分类方法

doi:10.11896/jsjkx.220300202

Abstract

Abstract: In order to improve the performance of cost-sensitive classification,this paper improves the learning accuracy of higher misclassification cost categories to reduce the total misclassification cost,uses support vector domain description(SVDD) to reali-ze cost sensitive classification,and proposes a cost sensitive SVDD two-class classification method,CS-SVDD.This method first expands single class SVDD to two class classification SVDD,and constructs SVDD hyperspheres for different categories.by adjusting the classification accuracy of SVDD classifier for different class samples through the misclassification cost,the class with high misclassification cost can be more accurately learned,so as to reduce the total misclassification cost.For the samples with ambiguous category attributes outside the two hyperspheres or in the coverage area,cost sensitive decision rules are defined based on the principle of minimum misclassification cost.Experimental results on artificial data sets and UCI data sets show the effectiveness of the proposed method.

Key words: Cost sensitive classification, Support vector data description, Support vector

CLC Number:

TP391

WU Chongming, WANG Xiaodan, ZHAO Zhenchong. New Cost Sensitive SVDD Binary Classification Method[J].Computer Science, 2023, 50(6A): 220300202-5.

References

[1]HABBEMA J D F,HERMANS J.Cases of doubt in allocation problems[J].Biometrika,1974,61(2):313-324.
[2]ELKAN C.The Foundations of Cost-Sensitive Learning[C]//Proceedings of Proceedings of 17th International Joint Confe-rence on Artificial Intelligence.2001:973-978.
[3]DHAR S,CHERKASSKY V.Development and evaluation ofcost-sensitive universum-SVM[J].IEEE Transactions on Cybernetics,2015,45(4):806-818.
[4]YUAN G L,SUN Z W,QIN X Y,et al.Object Tracking Based on Cost Sensitive Structured SVM[J].Journal of Electronics & Information Technology,2021,43(11):3335-3341.
[5]DATTA S,DAS S.Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs[J].Neural Networks,2015,70:39-52.
[6]IBÁÑEZ A,BIELZA C,LARRAÑAGA P.Cost-sensitive selective naïve Bayes classifiers for predicting the increase of the h-index for scientific journals[J].Neurocomputing,2014,135:42-52.
[7]JIANG L,LI C,WANG S.Cost-sensitive Bayesian network classifiers[J].Pattern Recognition Letters,2014,45:211-216.
[8]FREITAS A,COSTA-PEREIRA A,BRAZDIL P.Cost-sensitive decision trees applied to medical data[C]//Data Warehousing and Knowledge Discovery.Springer,2007:303-312.
[9]LI X J,ZHAO H,ZHU W.A cost sensitive decision tree algorithm with two adaptive mechanisms[J].Knowledge-Based Systems,2015,88:24-33.
[10]XIONG B Y,WANG G Y,DENG W B.Hierarchical Cost Sensi-tive Decision Tree and Its Application in Mobile Phone Replacement Prediction[J].Journal of Shandong University,2015,45(5):36-42.
[11]CHEN Y L,WUB C C,TANG K.Time-constrained cost-sensitive decision tree induction[J].Information Sciences,2016,354:140-152.
[12]SUN Y M.Cost-sensitive boosting for classification of imbal-anced data[J].Pattern Recognition,2007,40:3358-3378.
[13]XUE Y Z,WANG T.Target tracking based on cost Sensitive Adaboost[J].Chinese Journal of Image and Graphics,2016,21(5):544-555.
[14]CHENG F Y,ZHANG J,WEN C H.Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data[J].Pattern Recognition Letters,2016,80:107-112.
[15]CHENG F Y,ZHANG J.Large cost-sensitive margin distribution machine for imbalanced data classification[J].Neurocomputing,2017,224:45-57.
[16]TAX D M J,DUIN R P W.Support vector domain description[J].Pattern Recognition Letters,1999,20:1191-1199.
[17]TAX D M J,DUIN R P W.Support vector domain description[J].Machine Learning,2004,54:45-66.
[18]LEE D,LEE J.Domain described support vector classifier formulti-classification problems[J].Pattern Recognition,2007,40(1):41-51.
[19]MU T T,NANDI A K.Multiclass classification based on ex-tended support vector data description[J].IEEE Trans on Systems,Man,and Cybernetics－Part B:Cybernetics,2009,39(5):1206-1216.
[20]ZHENG S F.Smoothly approximated support vector domain description[J].Pattern Recognition,2016,49:55-64.
[21]GUO Y,XIAO H,FU Q.Least square support vector data description for HRRP-based radar target recognition[J].Applied Intelligence,2017,46:365-372.
[22]WANG C D,LAI J H.Position regularized Support Vector Domain Description[J].Pattern Recognition,2013,46:875-884.
[23]ElAZAMI M.Converting SVDD scores into probability esti-mates:Application to outlier detection[J/OL].http://dx.doi.org/10.1016/j.neucom.2017.01.103.
[24]DUAN L X,XIE M Y.A new support vector data description method for machinery fault diagnosis with unbalanced datasets[J].Expert Systems With Applications,2016,64:239-246.
[25]ZHOU Y M,WU K.Fault detection of aircraft based on support vector domain description[J].Computers and Electrical Engineering,2017,61:80-94.
[26]HUANG G,CHEN H,ZHOU Z,et al.Two-class support vector data description[J].Pattern Recognition,2011,44:320-329.

Related Articles 15

[1]	CUI Jingsong, ZHANG Tongtong, GUO Chi, GUO Wenfei. Network Equipment Anomaly Detection Based on Time Delay Feature [J]. Computer Science, 2023, 50(3): 371-379.
[2]	DU Hang-yuan, LI Duo, WANG Wen-jian. Method for Abnormal Users Detection Oriented to E-commerce Network [J]. Computer Science, 2022, 49(7): 170-178.
[3]	HOU Xia-ye, CHEN Hai-yan, ZHANG Bing, YUAN Li-gang, JIA Yi-zhen. Active Metric Learning Based on Support Vector Machines [J]. Computer Science, 2022, 49(6A): 113-118.
[4]	SHAN Xiao-ying, REN Ying-chun. Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm [J]. Computer Science, 2022, 49(6A): 211-216.
[5]	CHEN Jing-nian. Acceleration of SVM for Multi-class Classification [J]. Computer Science, 2022, 49(6A): 297-300.
[6]	XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[7]	YANG Hong-jian, HU Xue-xian, LI Ke-jia, XU Yang, WEI Jiang-hong. Study on Privacy-preserving Nonlinear Federated Support Vector Machines [J]. Computer Science, 2022, 49(12): 22-32.
[8]	DAI Xiao-lu, WANG Ting-hua, ZHOU Hui-ying. Fuzzy Multiple Kernel Support Vector Machine Based on Weighted Mahalanobis Distance [J]. Computer Science, 2022, 49(11A): 210800216-5.
[9]	WANG Jia-chang, ZHENG Dai-wei, TANG Lei, ZHENG Dan-chen, LIU Meng-juan. Empirical Research on Remaining Useful Life Prediction Based on Machine Learning [J]. Computer Science, 2022, 49(11A): 211100285-9.
[10]	GAO Ji-hang, ZHANG Yan. Fault Diagnosis of Shipboard Zonal Distribution Power System Based on FWA-PSO-MSVM [J]. Computer Science, 2022, 49(11A): 210800209-5.
[11]	GUO Fu-min, ZHANG Hua, HU Rong-hua, SONG Yan. Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals [J]. Computer Science, 2021, 48(6A): 317-320.
[12]	ZHUO Ya-qian, OU Bo. Face Anti-spoofing Algorithm for Noisy Environment [J]. Computer Science, 2021, 48(6A): 443-447.
[13]	LEI Jian-mei, ZENG Ling-qiu, MU Jie, CHEN Li-dong, WANG Cong, CHAI Yong. Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning [J]. Computer Science, 2021, 48(6): 190-195.
[14]	WANG You-wei, ZHU Chen, ZHU Jian-ming, LI Yang, FENG Li-zhou, LIU Jiang-chun. User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification [J]. Computer Science, 2021, 48(11A): 251-257.
[15]	YU Tian-qi, HU Jian-ling, JIN Jiong, YANG Jian-feng. Mobile Edge Computing Based In-vehicle CAN Network Intrusion Detection Method [J]. Computer Science, 2021, 48(1): 34-39.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

New Cost Sensitive SVDD Binary Classification Method

PDF (PC)