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Ant colony optimization combining with mutual information for feature selection in support vector machines

  • Harbin Institute of Technology Shenzhen

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

Abstract

An effective feature selection scheme is proposed, which utilizes the combination of wrapper and filter: ant colony optimization (ACO) and mutual information (MI). By examining the modeling based on SVMs at the Australian Bureau of Meteorology, the simulation of three different methods of feature selection shows that the proposed method can reduce the dimensionality of inputs, speed up the training of the network and get better performance.

Original languageEnglish
Title of host publicationAI 2005
Subtitle of host publicationAdvances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Pages918-921
Number of pages4
ISBN (Print)3540304622, 9783540304623
DOIs
StatePublished - 2005
Externally publishedYes
Event18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence - Sydney, Australia
Duration: 5 Dec 20059 Dec 2005

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

Conference

Conference18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
Country/TerritoryAustralia
CitySydney
Period5/12/059/12/05

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