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Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms - Okan Yilmaz

Anglų
2012-11-12
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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 accurac ... Visas aprašymas

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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.

Daugiau informacijos

Autorius Okan Yilmaz
Leidėjas Creative Media Partners, LLC
Išleidimo metai 2012
Viršelio tipas Minkšti viršeliai
EAN 9781288281657
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21,99 € 33,83 €