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A feature selection model for document classification using Tom and Jerry Optimization algorithm

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Abstract

Since the last decade, high-dimensional data has been increasing in various document mining fields, such as text summarization, text clustering, and text classification. The curse of dimensionality has an impact on the classification model’s performance. The feature selection strategy is extremely effective in dealing with the curse of dimensionality issue. In this work, we present the Tom and Jerry Optimization technique(TJO) for feature subset selection. The proposed work uses the classifier error rate and the feature chosen rate to measure the candidate’s fitness. The performance of the proposed scheme is examined using two popular benchmark text corpus and compared with five metaheuristic approaches. The best success rate obtained by the proposed scheme is 95.77%, whereas the best precision is 0.9509, recall is 0.9577 and F1-score is 0.9541. According to the comparison results, the proposed feature subset selection scheme outperforms the standard strategy.

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Data Availibility Statement

The dataset used in this study are available in a public repository

Notes

  1. https://data.mendeley.com/datasets/9rw3vkcfy4/6

  2. https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews

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Authors and Affiliations

  1. Mepco Schlenk Engineering College, Sivakasi, 626005, India

    K Thirumoorthy

  2. Ramco Institute of Technology, Rajapalayam, 626117, India

    J Jerold John Britto

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  1. K Thirumoorthy
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Thirumoorthy, K., Britto, J.J.J. A feature selection model for document classification using Tom and Jerry Optimization algorithm. Multimed Tools Appl 83, 10273–10295 (2024). https://doi.org/10.1007/s11042-023-15828-6

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