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 (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: © 2018 |Pages: 13
DOI: 10.4018/IJAMC.2018070104

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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In order to obtain better classification accuracy for the diagnosis of diseases with the Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, and Dermatology datasets, many machine learning algorithms have been successfully applied. In our research work, we are interested in the use of meta-heuristic for feature selection to improve the performance of classification algorithm. In this field, many researchers have developed various approaches.

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