An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering

TL;DR

This paper introduces a hybrid meta-heuristic algorithm combining TLBO, GWO, and GA operators to improve feature selection for text clustering, demonstrating superior performance on benchmark datasets.

Contribution

It proposes a novel hybrid feature selection algorithm (TLBO-GWO) that enhances text clustering effectiveness by combining multiple meta-heuristic techniques.

Findings

TLBO-GWO outperforms existing feature selection algorithms.

The hybrid approach improves clustering accuracy and reduces feature dimensions.

Statistical tests confirm the significance of the results.

Abstract

Text document clustering can play a vital role in organizing and handling the everincreasing number of text documents. Uninformative and redundant features included in large text documents reduce the effectiveness of the clustering algorithm. Feature selection (FS) is a well-known technique for removing these features. Since FS can be formulated as an optimization problem, various meta-heuristic algorithms have been employed to solve it. Teaching-Learning-Based Optimization (TLBO) is a novel meta-heuristic algorithm that benefits from the low number of parameters and fast convergence. A hybrid method can simultaneously benefit from the advantages of TLBO and tackle the possible entrapment in the local optimum. By proposing a hybrid of TLBO, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) operators, this paper suggests a filter-based FS algorithm (TLBO-GWO). Six benchmark datasets are selected, and TLBO-GWO is compared with three recently proposed FS algorithms with similar approaches, the main TLBO and GWO. The comparison is conducted based on clustering evaluation measures, convergence behavior, and dimension reduction, and is validated using statistical tests. The results reveal that TLBO-GWO can significantly enhance the effectiveness of the text clustering technique (K-means).