Journal of Guangxi Normal University(Natural Science Edition) ›› 2010, Vol. 28 ›› Issue (3): 104-108.

Previous Articles     Next Articles

Binary Classification with Misclassification Cost and Reject Cost

ZOU Chao1, ZHENG En-hui1, REN Yu-ling2, ZHANG Ying3, FAN Yu-gang4   

  1. 1. College of Mechatronics Engineering,China Jiliang University,Hangzhou Zhejiang 310018,China;
    2. Zhejiang Tianda Environmental Protection Co. LTD,Hangzhou Zhejiang 310006,China;
    3. IBM Global Services Co. LTD,Shanghai 200032,China;
    4. College of Information Engineering and Automatics,Kunming University of Science and Technology, Kunming Yunnan 650000,China
  • Received:2010-05-08 Online:2010-09-20 Published:2023-02-06

Abstract: To minimize “0-1” loss,most of conventional classification algorithmsnon-explicitly assume that all results of classification are accepted.However,the assumption is inappliable to knowledge extraction in such fields as medical diagnosis,fault diagnosis and fraud detection.In this paper,the algorithm Cost-sensitive SVM with Class-dependent Misclassification Cost and Class-dependent Reject Cost (CSVM-CMC2RC) is proposed.In CSVM-CMC2RC algorithm,firstly,acost-sensitive SVM is trained to obtain the preliminary classification results.Secondly,the post probability of each sample is computed.Thirdly,the classification reliability of each sample is estimated.Finally,the optimal reject threshold and the final reject decision are determined based on minimizing the average cost.Experimental results demonstrate that the proposed CSVM-CMC2RC algorithm can reduce the misclassification rate and average cost,and the classification reliability is improved.

Key words: class-dependent misclassification cost, class-dependent reject cost, cost-sensitive SVM

CLC Number: 

  • TP18
[1] VAPNIK V N.The nature of statistical learning theory[M].New York:Springer-Verlag,1999:138-167.
[2] 王修信,秦丽梅,罗涟玲,等.提高城市TM图像分类精度的两种方法比较[J].广西师范大学学报:自然科学版,2009,27(4):19-22.
[3] DOMINGOS P.Metacost:a general method for making classifiers cost-sensitive[C]//Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,1999:155-164.
[4] SáNCHEZ D,VILA M A,CERDA L,et al.Association rules applied tocreditcard fraud detection[J].Expert Systems with Applications,2009,36(2):3630-3640.
[5] SRIVASTAVA A,KUNDU A,SURAL S,et al.Credit card fraud detectionusing hidden Markov model[J].IEEE Transactions on Dependable and Secure Computing,2008,5(1):37-48.
[6] 周生明,廖元秀.Cost-Sensitive学习的一个新课题[J].广西师范大学学报:自然科学版,2007,25(4):55-58.
[7] ELKAN C.The foundations of cost-sensitive learning[C]//Proceedings of the 7th International Joint Conference on Artificial Intelligence.California:AAAIPress,2001:937-978.
[8] LING Xiao-feng,SHENG V S.A comparative study of cost-sensitive classifiers[J].Chinese Journal of Computers,2007,30(8):1203-1211.
[9] ZHOU Zhi-hua,LIU Xu-ying.On multi-class cost-sensitive learning[C]//Proceedings of the 21st National Conference on Artificial Intelligence.California:AAAI Press,2006,1:567-572.
[10] LIU Xu-ying,ZHOU Zhi-hua.The influence of class imbalance on cost-sensitive learning:an empirical study[C]//Proceedings of the Sixth International Conference on Data Mining.Las Vegas:CSREA Press,2006:970-974.
[11] FUMERA G,ROLI F.Cost-sensitive learning in support vector machines[C]//Proceedings of the Ⅷ Convegno Associazione Italiana per L' Intelligenza Artificiale.Bologna:Pitagora Editrice,2002.
[12] CHOW C K.On optimum recognition error and reject tradeoff[J].IEEE Transactions Information Theory,1970,16(1):41-46.
[13] FOGGIA P,SANSONE C,TORTORELLA F,et al.Multi-classification:reject criteria for the Bayesian combiner[J].Pattern Recognition,1999,32:1435-1447.
[14] STEFANO C D,SANSONE C,VENTO M.To reject or not to reject:thatis the question-answer in case of neural classifiers[J].IEEE Transactions on SMC,2008,30(1):84-94.
[15] LANDGREBE T C W,TAX D M J,PACLíK P,et al.The interaction between classification and reject performance for distance-based reject-option classifiers[J].Pattern Recognition Letters,2006,27(8):908-917.
[16] ZHENG En-hui,ZOU Chao,SUN Jian,et al.SVM-based credit card fraud detection with reject cost and class-dependent error cost[C]//Proceedings of thePAKDD' 09 Workshop:Data Mining When Classes are Imbalanced and Errors Have Cost.Rangsit Campus:Thammasat Printing House,2009:50-58.
[17] AHA D.UCI machine learning datasets[OL/DB].(2009-09-09).http://archive.ics.uci.edu/ml/datasets.html.
[1] DAI Jiayang, ZHOU Dong. Research on Cross-Language Information Retrieval Method Based on Multi-task Learning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(6): 69-81.
[2] WANG Ximin, YUAN Jie, KOU Qiaoyuan. An Improved Slime Mould Algorithm Based on Multi-Strategy [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(6): 98-108.
[3] XIAO Fei, KANG Zengyan, WANG Weihong. Two Algorithms for Prognosis of DenitrificationConditions of A2/O Technology [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(6): 173-184.
[4] ZHANG Shichao, LI Jiaye. Knowledge Matrix Representation [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(5): 36-48.
[5] DU Jinfeng, WANG Hairong, LIANG Huan, WANG Dong. Progress of Cross-modal Retrieval Methods Based on Representation Learning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 1-12.
[6] PENG Tao, TANG Jing, HE Kai, HU Xinrong, LIU Junping, HE Ruhan. Emotion Recognition Based on Multi-gait Feature Fusion [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 104-111.
[7] MA Xinna, ZHAO Men, QI Lin. Fault Diagnosis Based on Spiking Convolution Neural Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 112-120.
[8] JIANG Rui, XU Juan, LI Qiang. A Prediction Method of Bearing Remaining Useful Life Based on Cross Domain Mean Approximation [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 121-131.
[9] DUAN Meiling, PAN Julong. Wearable Fall Detection Based on Bi-directional LSTM Neural Network [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 141-150.
[10] KONG Yayu, LU Yujie, SUN Zhongtian, XIAO Jingxian, HOU Haochen, CHEN Tingwei. Research on Graph Neural Network Recommendation Algorithms for Reinforcing Current Interest [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 151-160.
[11] WU Jun, OUYANG Aijia, ZHANG Lin. Phosphorylation Site Prediction Model Based on Multi-head Attention Mechanism [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 161-171.
[12] CHEN Gaojian, WANG Jing, LI Qianwen, YUAN Yunjing, CAO Jiachen. Data-driven Method for Automatic Machine Learning Pipeline Generation [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 185-193.
[13] YANG Di, FANG Yangxin, ZHOU Yan. New Category Classification Research Based on MEB and SVM Methods [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(1): 57-67.
[14] TANG Fengzhu, TANG Xin, LI Chunhai, LI Xiaohuan. Dynamic Task Allocation Method for UAVs Based on Deep Reinforcement Learning [J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(6): 63-71.
[15] LU Kaifeng, YANG Yilong, LI Zhi. A Web Service Classification Method Using BERT and DPCNN [J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(6): 87-98.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Guangxi Normal University(Natural Science Edition), All Rights Reserved.
Tel: 0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
Powered by Beijing Magtech Co. Ltd