NDLI: Feature subset selection by particle swarm optimization with fuzzy fitness function

Content Provider	IEEE Xplore Digital Library
Author	Chakraborty, B.
Copyright Year	2008
Description	Author affiliation: Fac. of Software & Inf. Sci., Iwate Prefectural Univ., Takizawa, Japan (Chakraborty, B.)
Abstract	Feature extraction or feature subset selection is an important preprocessing task for pattern recognition, data mining or machine learning application. Feature subset selection basically depends on selecting a criterion function for evaluation of the feature subset and a search strategy to find the best feature subset from a large number of feature subsets. Lots of techniques have been developed so far, mainly from statistical theory, still research is going on to find better solutions in terms of optimality and computational ease. Recently soft computing techniques are gaining popularity for solving real world problems for their more flexibility compared to statistical or mathematical techniques. In this work an algorithm based on particle swarm optimization with fuzzy fitness function has been proposed for getting optimal feature subset from a feature set with large number of features. Simple simulation experiments with two benchmark data sets show that the proposed method is similar in performance to the results reported earlier and is computationally less demanding in comparison to genetic algorithm, another population based evolutionary search technique proposed earlier for feature subset selection by author.
Starting Page	1038
Ending Page	1042
File Size	1029644
Page Count	5
File Format	PDF
ISBN	9781424421961
DOI	10.1109/ISKE.2008.4731082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Learning systems Knowledge engineering Machine learning algorithms Filters Machine learning Data mining Particle swarm optimization Intelligent systems Genetic algorithms
Content Type	Text
Resource Type	Article

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2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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