Fuzzy classifier with Bayes rule consequent

Do Wan Kim, Jin Bae Park, Young Hoon Joo

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

Abstract

This paper proposes a new fuzzy rule-based classifier equipped with a Bayes rule consequent. The main features of our approach are no requirement on the covariance matrices structure and their avoidance of singularity; the expansion in unimodal densities to multimodal ones; and the fuzzy set analysis for measuring the qualities of features. Two tools are exploited in constructing the proposed classifier: the iterative pruning algorithm for removing the irrelevant features and the gradient descent method for training the related parameters.

Original languageEnglish
Title of host publicationAI 2005
Subtitle of host publicationAdvances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
Pages1130-1133
Number of pages4
DOIs
Publication statusPublished - 2005
Event18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia
Duration: 2005 Dec 52005 Dec 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3809 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
CountryAustralia
CitySydney
Period05/12/505/12/9

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Fuzzy classifier with Bayes rule consequent'. Together they form a unique fingerprint.

Cite this