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

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

Noria Bidi, Zakaria Elberrichi
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 13
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522544548|DOI: 10.4018/IJAMC.2018070104
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MLA

Bidi, Noria, and Zakaria Elberrichi. "Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm." IJAMC vol.9, no.3 2018: pp.75-87. http://doi.org/10.4018/IJAMC.2018070104

APA

Bidi, N. & Elberrichi, Z. (2018). Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm. International Journal of Applied Metaheuristic Computing (IJAMC), 9(3), 75-87. http://doi.org/10.4018/IJAMC.2018070104

Chicago

Bidi, Noria, and Zakaria Elberrichi. "Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC) 9, no.3: 75-87. http://doi.org/10.4018/IJAMC.2018070104

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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.

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