A feature selection model for document classification using Tom and Jerry Optimization algorithm
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 e...
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| Published in | Multimedia tools and applications Vol. 83; no. 4; pp. 10273 - 10295 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-023-15828-6 |
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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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| AbstractList | 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. |
| Author | Britto, J Jerold John Thirumoorthy, K |
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| ContentType | Journal Article |
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| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Tom and Jerry optimization Feature selection Parameter-free optimization Text classification |
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| References_xml | – reference: Bahassine S, Madani A, Al-Sarem M, Kissi M (2020) Feature selection using an improved chi-square for arabic text classification. J King Saud Univ Comput Inf Sci 32(2):225–231 . http://www.sciencedirect.com/science/article/pii/S131915781730544X – reference: Zhu, W, Feng, J, Lin, Y (2014) Using gini-index for feature selection in text categorization. In: Proceedings of the 2014 International conference on information, business and education technology, Atlantis Press, pp 76–80. https://doi.org/10.2991/icibet-14.2014.22 – reference: BehjatAMustaphaANezamabadi-pourHSulaiman, MNA PSO-based feature subset selection for application of spam/non-spam detection201337818319310.1007/978-3-642-40567-9_16 – reference: ThirumoorthyKMuneeswaranKOptimal feature subset selection using hybrid binary jaya optimization algorithm for text classificationSādhanā202045120110.1007/s12046-020-01443-w – reference: AdamSPAlexandropoulosSANPardalosPMVrahatisMNNo free lunch theorem: a review2019ChamSpringer578210.1007/978-3-030-12767-1_5 – reference: Yazdani M, Jolai F (2016) Lion optimization algorithm (loa): A nature-inspired metaheuristic algorithm. 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