Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms - Couverture souple

Yilmaz, Okan

 
9781288281657: Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms
Couverture souple
ISBN 10 : 128828165X ISBN 13 : 9781288281657
Editeur : BiblioBazaar, LLC, 2012

Synopsis

We present a simple genetic algorithm (sGA), which is developed under Genetic Rule and Classifier Construction Environment (GRaCCE) to solve feature subset selection and weighting problem to have better classification accuracy on k-nearest neighborhood (KNN) algorithm. Our hypotheses are that weighting the features will affect the performance of the KNN algorithm and will cause better classification accuracy rate than that of binary classification. The weighted-sGA algorithm uses real-value chromosomes to find the weights for features and binary-sGA uses integer-value chromosomes to select the subset of features from original feature set. Since we use real-value chromosomes for weighted-sGA, instead of using standard crossover and mutation operators, these GRaCCE sGA operators are modified to adjust them to the feature subset selection and weighting problem.

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Éditeur
BiblioBazaar, LLC
Date d'édition
2012
Langue
anglais
ISBN 10 
128828165X
ISBN 13 
9781288281657
Reliure
Broché
Nombre de pages
126
Coordonnées du fabricant
non disponible
Personne responsable
gpsr@libri.de
gpsr@libri.de

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