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Multiple Features Subset Selection using Meta-Heuristic Function

Keywords: Meta-Heuristic Function

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Abstract:

This paper is being presented on Multiple Features Subset Selection Using Meta-heuristic Function. Classification problems require selection of a subset of attributes or features from a much larger dataset to represent the patterns to be classified. Many excellent multiple feature selection method such as Hill Climbing (HC), Simulated Annealing (SA), Genetic Algorithms (GAs), Tabu Search (TS) has been prevalent amongst research community. However, these approaches still encounter when the multiple feature of dataset is available and it need to choose those attribute which is best amongst the available features. So, in this paper basically considering the issue of multiple features are analyzed as well as implemented and tested on different dataset. The experiment is being conducted abalone dataset. It may conclude that the algorithm shows highest accuracy amongst all other method which is being used in this paper, Therefore we are focusing on the classification of multiple features

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