Open AccessEfficient Feature Subset Selection Algorithm for High Dimensional Data
Author(s) -
Smita Chormunge,
Sudarson Jena
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i4.pp1880-1888
Subject(s) - feature selection , curse of dimensionality , computer science , feature (linguistics) , information gain , minimum redundancy feature selection , pattern recognition (psychology) , classifier (uml) , algorithm , naive bayes classifier , artificial intelligence , dimensionality reduction , data mining , support vector machine , philosophy , linguistics
Feature selection approach solves the dimensionality problem by removing irrelevant and redundant features. Existing Feature selection algorithms take more time to obtain feature subset for high dimensional data. This paper proposes a feature selection algorithm based on Information gain measures for high dimensional data termed as IFSA (Information gain based Feature Selection Algorithm) to produce optimal feature subset in efficient time and improve the computational performance of learning algorithms. IFSA algorithm works in two folds: First apply filter on dataset. Second produce the small feature subset by using information gain measure. Extensive experiments are carried out to compare proposed algorithm and other methods with respect to two different classifiers (Naive bayes and IBK) on microarray and text data sets. The results demonstrate that IFSA not only produces the most select feature subset in efficient time but also improves the classifier performance.