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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.<\/jats:p>","DOI":"10.4018\/ijamc.2018070104","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T13:02:16Z","timestamp":1523883736000},"page":"75-87","source":"Crossref","is-referenced-by-count":4,"title":["Best Features Selection for Biomedical Data Classification Using Seven Spot Ladybird Optimization Algorithm"],"prefix":"10.4018","volume":"9","author":[{"given":"Noria","family":"Bidi","sequence":"first","affiliation":[{"name":"Department of Science and Technology, University Mustapha Stamboli, Mascara, Algeria"}]},{"given":"Zakaria","family":"Elberrichi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University Djillali Liabes, Sidi Bel Abbes, Algeria"}]}],"member":"2432","reference":[{"key":"IJAMC.2018070104-0","unstructured":"Afzan, A., & Khairuddin, O. (2012). 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