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Fuzzy Mutual Information Feature Selection Based on Representative Samples

Fuzzy Mutual Information Feature Selection Based on Representative Samples

Omar A. M. Salem, Liwei Wang
Copyright: © 2018 |Volume: 6 |Issue: 1 |Pages: 15
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522546832|DOI: 10.4018/IJSI.2018010105
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MLA

Salem, Omar A. M., and Liwei Wang. "Fuzzy Mutual Information Feature Selection Based on Representative Samples." IJSI vol.6, no.1 2018: pp.58-72. http://doi.org/10.4018/IJSI.2018010105

APA

Salem, O. A. & Wang, L. (2018). Fuzzy Mutual Information Feature Selection Based on Representative Samples. International Journal of Software Innovation (IJSI), 6(1), 58-72. http://doi.org/10.4018/IJSI.2018010105

Chicago

Salem, Omar A. M., and Liwei Wang. "Fuzzy Mutual Information Feature Selection Based on Representative Samples," International Journal of Software Innovation (IJSI) 6, no.1: 58-72. http://doi.org/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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