Feature selection using ant lion optimization algorithm in text categorization

Abstract

This is Big Data decade with extensive increase in the textual information where the text classification is the significant approach for processing and organizing textual information. Text categorization refers to the process of spontaneously allotting documents to the relevant classes. The key features of these text classification issue is tremendous increase in higher dimensionality of text information. Meta-Heuristics Approaches are effortlessly employed to obtain optimal solutions for high dimensional datasets in text categorization. However, some of these approaches like genetic algorithm and particle swarm optimization gives a sub-optimal solutions, the convergence time is more compared to other approaches and cannot guarantee the global maxima to the text categorization. Thus, in this paper, a nature-inspired optimization approach depending on catching mechanism of antlions in the environment known as Ant Lion Optimizer (ALO) Approach, is applied to resolve higher dimensionality issues prior to text classification. The precision and recall values for the proposed is comparatively effective when compared with the existing text categorization dimensionality reduction techniques.