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Titolo:
A neuro fuzzy algorithm for feature subset selection
Autore:
Chakraborty, B; Chakraborty, G;
Indirizzi:
Iwate Prefectural Univ, Fac Software & Informat Sci, Morioka, Iwate 0200193, Japan Iwate Prefectural Univ Morioka Iwate Japan 0200193 , Iwate 0200193, Japan
Titolo Testata:
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
fascicolo: 9, volume: E84A, anno: 2001,
pagine: 2182 - 2188
SICI:
0916-8508(200109)E84A:9<2182:ANFAFF>2.0.ZU;2-#
Fonte:
ISI
Lingua:
ENG
Keywords:
feature subset selection; neuro fuzzy approach; fuzzy measure; feature ranking; fractal neural network;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
--discip_EC--
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: Chakraborty, B Iwate Prefectural Univ, Fac Software & Informat Sci, Morioka, Iwate 0200193, Japan Iwate Prefectural Univ Morioka Iwate Japan 0200193, Japan
Citazione:
B. Chakraborty e G. Chakraborty, "A neuro fuzzy algorithm for feature subset selection", IEICE T FUN, E84A(9), 2001, pp. 2182-2188

Abstract

Feature subset selection basically depends on the design of a criterion function to measure the effectiveness of a particular feature or a feature subset and the selection of a search strategy to find out the best feature subset. Lots of techniques have been developed so far which are mainly categorized into classifier independent falter approaches and classifier dependant wrapper approaches. Wrapper approaches produce good results but are computationally unattractive specially when nonlinear neural classifiers with complex learning algorithms are used. The present work proposes a hybrid two step approach for finding out the best feature subset from a large feature set in which a fuzzy set theoretic measure for assessing the goodness of a feature is used in conjunction with a multilayer perceptron (MLP) or fractal neural network (FNN) classifier to take advantage of both the approaches. Though the process does not guarantee absolute optimality, the selected feature subset produces near optimal results for practical purposes. The process is less tune consuming and computationally light compared to any neuralnetwork classifier based sequential feature subset selection technique. The proposed algorithm has been simulated with two different data sets to justify its effectiveness.

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Documento generato il 02/04/20 alle ore 22:15:32