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A genetic algorithm-based method for feature subset selection

A genetic algorithm-based method for feature subset selection. Feng Tan; Xuezheng Fu; Yanqing Zang; Anu G. Bourgeois Springer Soft Comput (2008) 12:111-120 Yi-Chia Lan. Outline. Introduction Feature selection methods Entropy-based feature ranking T-statistics

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A genetic algorithm-based method for feature subset selection

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  1. A genetic algorithm-based method for feature subset selection Feng Tan; Xuezheng Fu; Yanqing Zang; Anu G. Bourgeois Springer Soft Comput (2008) 12:111-120 Yi-Chia Lan

  2. Outline • Introduction • Feature selection methods • Entropy-based feature ranking • T-statistics • SVM-RFE(Recursive Feature Elimination) • Framework of feature selection algorithm • Experiments and results

  3. Introduction (cont.)

  4. Introduction (cont.) Training data (sets) Classificatory accuracy Test data (sets)

  5. Introduction • 1. Feature selection • Removing redundant irrelevant or noise features • Improve the predictive accuracy • 2. The experimental result demonstrate: • Higher classification accuracy • Minimize size of feature subsets

  6. Feature selection and extraction

  7. Feature selection methods (cont.) Entropy-based α : parameter : average distance among the instances : Euclidean distance between the two instances

  8. Feature selection methods (cont.) T-statistics

  9. Feature selection methods SVM-RFE At the optimum of J , the first order is neglected second order becomes

  10. Genetic algorithm

  11. Framework of feature selection algorithm (cont.)

  12. Framework of feature selection algorithm Fitness function : x : feature vector representing ; c(x) : classification accuracy w : parameter {0~1} ; s(x) : weighted size Crossover : Single-point crossover operator Mutation : 0.001

  13. Experiment result (1)

  14. Experiment result (2)

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