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This paper investigates the effectiveness of the Side-Blotched Lizard Optimization Algorithm (SBLA) for feature selection by developing six novel variants: Sbla-s1, Sbla-s2, Sbla-s3, Sbla-v1, Sbla-v2, and Sbla-v3, each employing distinct S-shaped or V-shaped transfer functions to convert the continuous search space to a binary format. These variants were rigorously evaluated on nineteen benchmark datasets from the UCI repository, comparing their performance based on average classification accuracy, average number of selected features, and average fitness value. The results demonstrated the superiority of Sbla-s3, achieving an average classification accuracy of 92.8% across all datasets, a mean number of selected features of 20, and an average fitness value of 0.08. Furthermore, Sbla-s3 consistently outperformed six other state-of-the-art metaheuristic algorithms, achieving the highest average accuracy on sixteen out of nineteen datasets. These findings establish Sbla-s3 as a promising and effective approach for feature selection, capable of identifying relevant features while maintaining high classification accuracy, potentially leading to improved model performance in various machine learning applications.<\/jats:p>","DOI":"10.1007\/s11042-024-20221-y","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T08:07:29Z","timestamp":1728288449000},"page":"29097-29117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Transfer function adaptation for effective feature selection with the side-blotched lizard algorithm"],"prefix":"10.1007","volume":"84","author":[{"given":"Mohamed","family":"Eisa","sequence":"first","affiliation":[]},{"given":"Marwa","family":"Abdel-lah","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-7300","authenticated-orcid":false,"given":"Fifi","family":"Farouk","sequence":"additional","affiliation":[]},{"given":"Amr","family":"Abdel-aal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"20221_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.knosys.2017.10.028","volume":"140","author":"E Hancer","year":"2018","unstructured":"Hancer E, Bing X, Mengjie Z (2018) Differential evolution for filter feature selection based on information theory and feature ranking. 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