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portada Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms
Type
Physical Book
Author
Publisher
Language
English
Pages
128
Format
Paperback
Dimensions
24.6x18.9x0.7 cm
Weight
0.24 kg.
ISBN13
9781288281657
Categories

Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms

Okan Yilmaz (Author) · Biblioscholar · Paperback

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

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Synopsis "Data Mining Feature Subset Weighting and Selection Using Genetic Algorithms"

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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