Towards simple, easy-to-understand, yet accurate classifiers

Doina Caragea, Dianne Cook, Vasant Honavar

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

10 Scopus citations

Abstract

We design a method for weighting linear support vector machine classifiers or random hyperplanes, to obtain classifiers whose accuracy is comparable to the accuracy of a non-linear support vector machine classifier, and whose results can be readily visualized. We conduct a simulation study to examine how our weighted linear classifiers behave in the presence of known structure. The results show that the weighted linear classifiers might perform well compared to the non-linear support vector machine classifiers, while they are more readily interpretable than the non-linear classifiers.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages497-500
Number of pages4
StatePublished - Dec 1 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Caragea, D., Cook, D., & Honavar, V. (2003). Towards simple, easy-to-understand, yet accurate classifiers. In Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003 (pp. 497-500). (Proceedings - IEEE International Conference on Data Mining, ICDM).