Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data
Renuka Devi D.1, Sasikala S.2
1D.Renuka Devi, Department of Computer Science, IDE, University of Madras, Chennai, India.
2Dr. Sasikala. S, Department of Computer Science, IDE, University of Madras, Chennai, India.
Manuscript received on 14 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 July 2019 | PP: 910-916 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1712078219/19©BEIESP | DOI: 10.35940/ijrte.B1712.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.
Index Terms: Accelerated Simulated Annealing and Mutation Operator (ASAMO), Big Data, Feature Selection, Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC), K-Nearest Neighbor (KNN) Classifier, Swarm Intelligence
Scope of the Article: Big Data