Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review 
Aakriti Shukla1, Damodar Prasad Tiwari2
1Aakriti Shukla, Department of Computer Science and Engineering, Bansal Institute of Science & Technology, Bhopal (M.P.), India.
2Dr Damodar Prasad Tiwari, Department of Computer Science and Engineering, Bansal Institute of Science & Technology, Bhopal (M.P.), India
Manuscript received on 29 November 2021 | Revised Manuscript received on 10 December 2021 | Manuscript Accepted on 15 December 2021 | Manuscript published on 30 December 2021 | PP: 1-4 | Volume-1 Issue-5, December 2021 | Retrieval Number: 100.1/ijainn.E1040101521 | DOI: 10.54105/ijainn.E1040.121521
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Abstract: Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.
Keywords: Big Data, Feature Selection, Optimization, Data Mining.
Scope of the Article: Big Data and Ai Approaches