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.

特征选择是高维文本分类问题中的重要任务。如今，大多数特征选择方法都使用优化算法的重要性来从高维特征空间中选择特征的最佳子集。最佳特征子集减少了计算成本并提高了文本分类器的准确性。在本文中，我们提出了一种基于归一化差异度量和二进制Jaya优化算法（NDM-BJO）的混合特征选择方法，以从文本语料库中获取适当的最佳特征子集。我们使用错误率作为最小化目标函数来衡量解决方案的适用性。使用朴素贝叶斯和支持向量机分类器以及各种流行的基准文本语料库数据集来评估提名的最佳特征子集。