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

Doina Caragea, Dianne Cook, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

10 Citations (Scopus)


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
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Number of pages4
Publication statusPublished - 1 Dec 2003
EventIEEE International Conference on Data Mining 2003 - Melbourne, United States of America
Duration: 19 Nov 200322 Nov 2003
Conference number: 3rd (Proceedings)


ConferenceIEEE International Conference on Data Mining 2003
Abbreviated titleICDM 2003
Country/TerritoryUnited States of America
Internet address

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