A constrained genetic algorithm for efficient dimensionality reduction for pattern classification

Rajesh Panicker, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In automated pattern recognition systems, the two main challenges are feature selection and extraction. The features selected directly affects the number of measurements required; and extracting low-dimensional features from the selected ones reduces the computational complexity of the classifier In traditional approaches, human expertise is obligatory for feature selection and statistical techniques are employed for feature projection. In this paper, a constrained genetic algorithm for performing these two tasks simultaneously, in conjunction with the k-nearest neighbor classifier is proposed. This algorithm requires minimal human intervention as it realizes good tradeoff solutions between classification. accuracy, feature measurement requirements, and computational complexity.
Original languageEnglish
Title of host publicationInternational Conference on Computational Intelligence and Security CIS’2007
Publication date2007
Pages424-427
ISBN (Print)978-0-7695-3072-7
DOIs
Publication statusPublished - 2007
Externally publishedYes
EventInternational Conference on Computational Intelligence and Security CIS’2007 -
Duration: 1 Jan 2007 → …

Conference

ConferenceInternational Conference on Computational Intelligence and Security CIS’2007
Period01/01/2007 → …

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