Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms - Softcover

Yilmaz, Okan

Softcover

ISBN 10: 128828165X ISBN 13: 9781288281657

Publisher: Biblioscholar, 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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Edition details

• Publisher: Biblioscholar
• Publication date: 2012
• Language: English
• ISBN 10: 128828165X
• ISBN 13: 9781288281657
• Binding: Paperback
• Number of pages: 126