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

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Author(s): Noria Bidi (Department of Science and Technology, University Mustapha Stamboli, Mascara, Algeria)and Zakaria Elberrichi (Department of Computer Science, University Djillali Liabes, Sidi Bel Abbes, Algeria)
Copyright: 2020
Pages: 15
Source title: Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch021

Keywords: Artificial Intelligence / Cognitive Science / Engineering Science Reference / Social Sciences & Humanities

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

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