Misclassification minimization based on multiple criteria linear programming

Bo Wang, Yong Shi, Wayne Wei Huang, Guanfeng Liu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Misclassification minimization is an important and interesting topic in classification problem. Obviously, exploring the solution for this topic will benefit to many real life problems, such as credit card clients classification. This paper focuses on misclassification minimization based on multiple criteria linear programming (MCLP), proposing two different schemes to minimize the number of misclassified points in original MCLP. Especially, the complementarity is used to construct the first scheme and linear approximation technique is applied to solve it. Furthermore, successive linearization algorithm (SLA) is employed to achieve minimization the second scheme. Finally, numerical experiment tests the effect of this idea.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Data Mining Workshop
Subtitle of host publicationProeccdings
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
Place of PublicationLos Alamitos
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages88-92
Number of pages5
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Data Mining (14th : 2014) - Shenzhen, China
Duration: 14 Dec 201417 Dec 2014

Conference

ConferenceIEEE International Conference on Data Mining (14th : 2014)
CountryChina
CityShenzhen
Period14/12/1417/12/14

Keywords

  • linear approximation
  • Misclassification minimization
  • multiple criteria linear programming
  • Successive linearization algorithm

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  • Cite this

    Wang, B., Shi, Y., Huang, W. W., & Liu, G. (2014). Misclassification minimization based on multiple criteria linear programming. In Z-H. Zhou, W. Wang, R. Kumar, H. Toivonen, J. Pei, J. Z. Huang, & X. Wu (Eds.), 2014 IEEE International Conference on Data Mining Workshop: Proeccdings (pp. 88-92). Los Alamitos: Institute of Electrical and Electronics Engineers (IEEE). IEEE International Conference on Data Mining Workshops, ICDMW https://doi.org/10.1109/ICDMW.2014.10