A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine |
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| Affiliation: | 1. Department of I.T., K.L.N. College of Information Technology, Tamil Nadu, India;2. School of Computing Science and Engg., VIT University – Chennai Campus, Tamil Nadu, India;1. Division of Ophthalmology, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania;2. Scheie Eye Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania;1. Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, 11300 Montevideo, Uruguay;2. Depto. de Lenguajes y Ciencias de la Computación, Univ. de Málaga, E.T.S. Ingeniería Informática, Campus de Teatinos, 29071 Málaga, Spain;1. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Duy Tân University, 254 Nguyen Van Linh Road, Da Nang, Viet Nam;3. ICTEAM, Université Catholique de Louvain, 4-6 Avenue G. Lemaître, B-1348 Louvain-La-Neuve, Belgium;1. Support Center for Advanced Neuroimaging – Institute for Diagnostic and Interventional Neuroradiology, University Hospital Inselspital and University of Bern, Bern, Switzerland;2. Department of Radiology, Division of Diagnostic and Interventional Neuroradiology, University Hospital, Basel, Switzerland;3. Institute of Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland,;4. Department of Neurosurgery, University Hospital Inselspital and University of Bern, Bern, Switzerland;1. Institute of Applied Mathematics and Information Technologies, CNR-IMATI, via Bassini 15, 20133 Milan, Italy;2. European Centre for Living Technology, Ca’ Foscari University of Venice, San Marco 2940, 30124 Venice, Italy;3. Department of Environmental Science, Informatics and Statistics, Ca’ Foscari University of Venice, Dorsoduro 2137, 30123 Venice, Italy;4. Department of Biology, University of Padua, Via U. Bassi 58, 35121 Padua, Italy;5. Explora Biotech S.r.l., Via della Libertá 9, 30175 Venice, Italy |
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| Abstract: | In classification, every feature of the data set is an important contributor towards prediction accuracy and affects the model building cost. To extract the priority features for prediction, a suitable feature selector is schemed. This paper proposes a novel memetic based feature selection model named Shapely Value Embedded Genetic Algorithm (SVEGA). The relevance of each feature towards prediction is measured by assembling genetic algorithms with shapely value measures retrieved from SVEGA. The obtained results are then evaluated using Support Vector Machine (SVM) with different kernel configurations on 11 + 11 benchmark datasets (both binary class and multi class). Eventually, a contrasting analysis is done between SVEGA-SVM and other existing feature selection models. The experimental results with the proposed setup provides robust outcome; hence proving it to be an efficient approach for discovering knowledge via feature selection with improved classification accuracy compared to conventional methods. |
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| Keywords: | Data mining Classification Svega-svm Feature selection Shapley values Genetic algorithm Memetic algorithm |
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