Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm

Noria Bidi and Zakaria Elberrichi
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Noria Bidi: Department of Science and Technology, University Mustapha Stamboli, Mascara, Algeria
Zakaria Elberrichi: Department of Computer Science, University Djillali Liabes, Sidi Bel Abbes, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2018, vol. 9, issue 3, 75-87

Abstract: This article presents a new adaptive algorithm called FS-SLOA (Feature Selection-Seven Spot Ladybird Optimization Algorithm) which is a meta-heuristic feature selection method based on the foraging behavior of a seven spot ladybird. The new efficient technique has been applied to find the best subset features, which achieves the highest accuracy in classification using three classifiers: the Naive Bayes (NB), the Nearest Neighbors (KNN) and the Support Vector Machine (SVM). The authors' proposed approach has been experimented on four well-known benchmark datasets (Wisconsin Breast cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets) taken from the UCI machine learning repository. Experimental results prove that the classification accuracy of FS-SLOA is the best performing for different datasets.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:9:y:2018:i:3:p:75-87

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