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DOI: 10.14569/IJACSA.2017.080656
PDF

A Feature Selection Algorithm based on Mutual Information using Local Non-uniformity Correction Estimator

Author 1: Ahmed I. Sharaf
Author 2: Mohamed Abu El-Soud
Author 3: Ibrahim El-Henawy

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 6, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Feature subset selection is an effective approach used to select a compact subset of features from the original set. This approach is used to remove irrelevant and redundant features from datasets. In this paper, a novel algorithm is proposed to select the best subset of features based on mutual information and local non-uniformity correction estimator. The proposed algorithm consists of three phases: in the first phase, a ranking function is used to measure the dependency and relevance among features. In the second phase, candidates with higher dependency and minimum redundancy are selected to participate in the optimal subset. In the last phase, the produced subset is refined using forward and backward wrapper filter to ensure its effectiveness. A UCI machine repository datasets are used for validation and testing. The performance of the proposed algorithm has been found very significant in terms of classification accuracy and time complexity.

Keywords: Feature subset selection; irrelevant features; mutual information; local non-uniformity correction

Ahmed I. Sharaf, Mohamed Abu El-Soud and Ibrahim El-Henawy, “A Feature Selection Algorithm based on Mutual Information using Local Non-uniformity Correction Estimator” International Journal of Advanced Computer Science and Applications(IJACSA), 8(6), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080656

@article{Sharaf2017,
title = {A Feature Selection Algorithm based on Mutual Information using Local Non-uniformity Correction Estimator},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080656},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080656},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {6},
author = {Ahmed I. Sharaf and Mohamed Abu El-Soud and Ibrahim El-Henawy}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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