a genetic algorithm-based method for feature subset selection
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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OutlineIntroduction
Feature selection methods
Entropy-based feature ranking T-statistics SVM-RFE(Recursive Feature
Elimination)
Framework of feature selection algorithm
Experiments and results
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Introduction (cont.)
Training data (sets)
Test data (sets)
Classificatory accuracy
Introduction (cont.)
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
Feature selection and extraction
Feature selection methods (cont.) Entropy-based
α : parameter
: average distance among the instances
: Euclidean distance between the two instances
Feature selection methods (cont.) T-statistics
Feature selection methods SVM-RFE
At the optimum of J , the first order is neglected
second order becomes
Genetic algorithm
Framework of feature selection algorithm (cont.)
Fitness function :
x : feature vector representing ; c(x) : classification accuracyw : parameter {0~1} ; s(x) : weighted size
Framework of feature selection algorithm
Crossover : Single-point crossover operator
Mutation : 0.001
Experiment result (1)
Experiment result (2)