A genetic algorithm-based method for feature subset selection

Abstract

As a commonly used technique in data preprocessing, feature selection selects a subset of informative attributes or variables to build models describing data. By removing redundant and irrelevant or noise features, feature selection can improve the predictive accuracy and the comprehensibility of the predictors or classifiers. Many feature selection algorithms with different selection criteria has been introduced by researchers. However, it is discovered that no single criterion is best for all applications. In this paper, we propose a framework based on a genetic algorithm (GA) for feature subset selection that combines various existing feature selection methods. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. We conducted experiments using three data sets and three existing feature selection methods. The experimental results demonstrate that our approach is a robust and effective approach to find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm.

A Hybrid Approach for Feature Subset Selection using Ant Colony Optimization and Multi-Classifier Ensemble

“…The present techniques are grouped into two categories: filters and wrappers on the basis of search strategy and subset evaluation method [9], [10]. Some existing filter and wrapper based approaches are described here: Feng Tan et al presented a framework for feature subset selection based on genetic algorithm [11]. The proposed algorithm rank features using entropy and T-statistics as a ranking criterion and select features on the basis of their rank.…”

The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

“…In this regard, there are some studies about GA application in feature selection. Combining multiple classifier based on genetic algorithm [45], using GA in input variable selection [46], applying GA in variable selection with customer clustering [47] and use of GA to combine feature selection methods [48]. Furthermore, there are some studies about using GA to build decision trees that are provided accordingly.…”

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

“…Finding a subset of features with sufficiently large discrimination power requires a very large search space. GA is very effective in solving large-scale problems, and can be used to find an optimal or near optimal feature subset [23]. In GA, the individuals are typically represented by nbit binary vectors.…”