A genetic algorithm-based method for feature subset selection

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

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

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

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

11. Genetic algorithm

12. Framework of feature selection algorithm (cont.)

13. 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

14. Experiment result (1)

15. Experiment result (2)