Robust feature selection for cancer microarray data using a hybrid mRMR and Binary Lion Optimization Algorithm
Rattachement africain : in, us, np. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Cancer microarray datasets often contain many irrelevant, duplicate, and even noisy features, which are likely to reduce the accuracy of classification algorithms. As a branch of feature engineering, the feature selection process aims to improve the classification performance of the desired microarray analysis by restricting the number of features to only those that are specified and valuable. Feature selection is an NP-hard problem, and agents searching for solutions often fall into local optima, requiring increasing time and effort to compute. This implies that a well-designed global search strategy is of utmost importance. Lion optimization (LO) is a recently proposed metaheuristic for global optimization. Due to its biologically inspired pride-based social structure, LO is capable of strong exploration through a nomadic search while ensuring exploitation through cooperative hunting mechanisms. It is well-balanced for optimal feature subset selection in high-dimensional datasets. However, the LO methodology seems to be constructed for continuous optimization tasks. To address this limitation, a variant algorithm, binary LO (BLO), was developed using an S-shaped Transfer Function to address wrapping-based feature selection in microarray cancer datasets. The proposed method was tested on 11 benchmark datasets on cancer microarrays that represent a variety of tumors and high-dimensional feature spaces. In this study, mRMR (Minimum Redundancy Maximum Relevance) is used as a filter method to reduce dimensionality before the wrapper-based BLO optimization method. The efficacy of the mRMR-BLO approach was evaluated across several prominent cancer datasets. It was also compared to four newer binary optimization techniques to test its effectiveness. Various performance metrics, such as Accuracy, MCR, Precision, Recall, Specificity, FNR, FPR, and MCC, are used to evaluate the model. Non parametric Wilcoxon Paired Signed Ranks test is performed to evaluate mRMR-BLO. The results showed that, with smaller feature sets, the proposed mRMR-BLO algorithm achieved the highest prediction accuracy among the compared existing optimization techniques.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Robust feature selection for cancer microarray data using a hybrid mRMR and Binary Lion Optimization Algorithm
- Date Crossref
- 18/05/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Symbiosis International University pays non établi dans la noticeUniversité ou école supérieure
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Siksha O Anusandhan University pays non établi dans la noticeUniversité ou école supérieure
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Capella University Department of Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Kathmandu University Electrical and Electronics Engineering pays non établi dans la noticeUniversité ou école supérieure
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Symbiosis International (Deemed University) Symbiosis Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
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Siksha O Anusandhan (Deemed to be University) Department of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
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Vardhaman College of Engineering Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Harvard T H Chan School of Public Health Environmental Health pays non établi dans la noticeUniversité ou école supérieure
Symbiosis International University, Siksha O Anusandhan University et Department of Information Technology — Capella University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.