An enhanced Teaching-Learning-Based Optimization (TLBO) with Grey Wolf Optimizer (GWO) for text feature selection and clustering
Mahsa Azarshab, Mohammad Fathian, Babak Amiri

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEdcuational Technology Systems
MethodsFeature Selection
