Optimal feature subset selection using hybrid binary Jaya optimization algorithm for text classification

Feature selection is an important task in the high-dimensional problem of text classification. Nowadays most of the feature selection methods use the significance of optimization algorithm to select an optimal subset of feature from the high-dimensional feature space. Optimal feature subset reduces...

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Bibliographic Details
Published inSadhana (Bangalore) Vol. 45; no. 1
Main Authors Thirumoorthy, K, Muneeswaran, K
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.12.2020
Springer Nature B.V
Subjects
Algorithms
Classification
Classifiers
Engineering
Feature selection
Optimization
Optimization algorithms
Support vector machines
dimensionality reduction
text classification
Hybrid feature selection
binary Jaya optimization
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Summary:Feature selection is an important task in the high-dimensional problem of text classification. Nowadays most of the feature selection methods use the significance of optimization algorithm to select an optimal subset of feature from the high-dimensional feature space. Optimal feature subset reduces the computation cost and increases the text classifier accuracy. In this paper, we have proposed a new hybrid feature selection method based on normalized difference measure and binary Jaya optimization algorithm (NDM-BJO) to obtain the appropriate subset of optimal features from the text corpus. We have used the error rate as a minimizing objective function to measure the fitness of a solution. The nominated optimal feature subsets are evaluated using Naive Bayes and Support Vector Machine classifier with various popular benchmark text corpus datasets. The observed results have confirmed that the proposed work NDM-BJO shows auspicious improvements compared with existing work.
ISSN:0256-2499
0973-7677
DOI:10.1007/s12046-020-01443-w