Integration of fuzzy classifiers with decision trees

I. J. Chiang, J. Y. Hsu

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

10 Citations (Scopus)

Abstract

It is often difficult to make accurate predictions given uncertain and noisy data for classification. Unfortunately, most real-world problems have to deal with such imperfect data. This paper presents a new model for fuzzy classification by integrating fuzzy classifiers with decision trees. In this approach, a fuzzy classification tree is constructed from the training data set. Instead of defining a specific class for a given instance, the proposed fuzzy classification scheme computes its degree of possibility for each class. The performance of the system is evaluated by empirically compared with a standard decision tree classifier C4.5 on several benchmark data sets the UCI machine learning repository.

Original languageEnglish
Title of host publicationProceedings of the Asian Fuzzy Systems Symposium
EditorsY.Y. Chen, K. Hirota, J.Y. Yen
PublisherIEEE
Pages266-271
Number of pages6
Publication statusPublished - 1996
Externally publishedYes
EventProceedings of the 1996 Asian Fuzzy Systems Symposium - Kenting, Taiwan
Duration: Dec 11 1996Dec 14 1996

Other

OtherProceedings of the 1996 Asian Fuzzy Systems Symposium
CityKenting, Taiwan
Period12/11/9612/14/96

ASJC Scopus subject areas

  • Engineering(all)

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