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

Abstract

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.

Nomenclature

i :

solution index

j :

position index

t :

iteration/generation index

f :

number of features to be selected

$T_{max}$ :

maximum number of iterations/generations

$\Psi _{i}$ :

$i^{\mathrm{th}}$ solution

$\Psi _{i,j}$ :

$j^{\mathrm{th}}$ position of solution $\Psi _{i}$

$\Psi _{i}^{(t)}$ :

$i^{\mathrm{th}}$ solution $\Psi _{i}$ of iteration/generation t

$\Psi _{i}^{fitness}$ :

fitness value of solution $\Psi _{i}$

$\Psi _{best}$ :

best solution

$\Psi _{best}^{fitness}$ :

fitness value of best solution

$\Psi _{worst}$ :

worst solution

$\Psi _{worst}^{fitness}$ :

fitness value of worst solution

$\alpha , \beta$ :

random numbers in [0,1]