A Distributed Minimum Redundancy Maximum Relevance Feature Selection Approac
Publish place: Tabriz Journal of Electrical Engineering، Vol: 51، Issue: 2
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: Persian
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JR_TJEE-51-2_014
تاریخ نمایه سازی: 1 آذر 1400
Abstract:
Feature selection (FS) is served in almost all data mining applications along with some benefits such as reducing the computation and storage cost. Most of the current feature selection algorithms just work in a centralized manner. However, this process does not apply to high dimensional datasets, effectively. In this paper, we propose a distributed version of Minimum Redundancy Maximum Relevance (mRMR) algorithm. The proposed algorithm acts in six steps to solve the problem. It distributes datasets horizontally into subsets, selects and eliminates redundant features, and finally merges the subsets into a single set. We evaluate the performance of the proposed method using different datasets. The results prove that the suggested method can improve classification accuracy and reduce the runtime
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Authors
مونا شریفی نژاد
Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran.
محسن رحمانی
Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran.
حسین غفاریان
Department of Computer Engineering, Faculty of Engineering, .Arak University, Arak, Iran.
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