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Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E85-A No.9 pp.2089-2092

- Publication Date
- 2002/09/01

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section LETTER (Special Section on Nonlinear Theory and Its Applications)

- Category

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Basabi CHAKRABORTY, "Genetic Algorithm with Fuzzy Operators for Feature Subset Selection" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 9, pp. 2089-2092, September 2002, doi: .

Abstract: Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_9_2089/_p

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@ARTICLE{e85-a_9_2089,

author={Basabi CHAKRABORTY, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Genetic Algorithm with Fuzzy Operators for Feature Subset Selection},

year={2002},

volume={E85-A},

number={9},

pages={2089-2092},

abstract={Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.},

keywords={},

doi={},

ISSN={},

month={September},}

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TY - JOUR

TI - Genetic Algorithm with Fuzzy Operators for Feature Subset Selection

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 2089

EP - 2092

AU - Basabi CHAKRABORTY

PY - 2002

DO -

JO - IEICE TRANSACTIONS on Fundamentals

SN -

VL - E85-A

IS - 9

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - September 2002

AB - Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

ER -