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Fuzzy Mutual Information Feature Selection Based on Representative Samples

Fuzzy Mutual Information Feature Selection Based on Representative Samples
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Author(s): Omar A. M. Salem (Faculty of Computer Science and Informatics, Suez Canal University, Ismailia, Egypt)and Liwei Wang (International School of Software, Wuhan University, Wuhan, China)
Copyright: 2018
Volume: 6
Issue: 1
Pages: 15
Source title: International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.2018010105

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Abstract

Building classification models from real-world datasets became a difficult task, especially in datasets with high dimensional features. Unfortunately, these datasets may include irrelevant or redundant features which have a negative effect on the classification performance. Selecting the significant features and eliminating undesirable features can improve the classification models. Fuzzy mutual information is widely used feature selection to find the best feature subset before classification process. However, it requires more computation and storage space. To overcome these limitations, this paper proposes an improved fuzzy mutual information feature selection based on representative samples. Based on benchmark datasets, the experiments show that the proposed method achieved better results in the terms of classification accuracy, selected feature subset size, storage, and stability.

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