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

Fingerprint

Decision trees
Classifiers
Learning systems

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Chiang, I. J., & Hsu, J. Y. (1996). Integration of fuzzy classifiers with decision trees. In Y. Y. Chen, K. Hirota, & J. Y. Yen (Eds.), Proceedings of the Asian Fuzzy Systems Symposium (pp. 266-271). IEEE.

Integration of fuzzy classifiers with decision trees. / Chiang, I. J.; Hsu, J. Y.

Proceedings of the Asian Fuzzy Systems Symposium. ed. / Y.Y. Chen; K. Hirota; J.Y. Yen. IEEE, 1996. p. 266-271.

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

Chiang, IJ & Hsu, JY 1996, Integration of fuzzy classifiers with decision trees. in YY Chen, K Hirota & JY Yen (eds), Proceedings of the Asian Fuzzy Systems Symposium. IEEE, pp. 266-271, Proceedings of the 1996 Asian Fuzzy Systems Symposium, Kenting, Taiwan, 12/11/96.
Chiang IJ, Hsu JY. Integration of fuzzy classifiers with decision trees. In Chen YY, Hirota K, Yen JY, editors, Proceedings of the Asian Fuzzy Systems Symposium. IEEE. 1996. p. 266-271
Chiang, I. J. ; Hsu, J. Y. / Integration of fuzzy classifiers with decision trees. Proceedings of the Asian Fuzzy Systems Symposium. editor / Y.Y. Chen ; K. Hirota ; J.Y. Yen. IEEE, 1996. pp. 266-271
@inproceedings{2ed8fe246f0b4c1a830530d335dfa3b4,
title = "Integration of fuzzy classifiers with decision trees",
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.",
author = "Chiang, {I. J.} and Hsu, {J. Y.}",
year = "1996",
language = "English",
pages = "266--271",
editor = "Y.Y. Chen and K. Hirota and J.Y. Yen",
booktitle = "Proceedings of the Asian Fuzzy Systems Symposium",
publisher = "IEEE",

}

TY - GEN

T1 - Integration of fuzzy classifiers with decision trees

AU - Chiang, I. J.

AU - Hsu, J. Y.

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0030350085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030350085&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030350085

SP - 266

EP - 271

BT - Proceedings of the Asian Fuzzy Systems Symposium

A2 - Chen, Y.Y.

A2 - Hirota, K.

A2 - Yen, J.Y.

PB - IEEE

ER -