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

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.

@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},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Ahmed I. Sharaf and Mohamed Abu El-Soud and Ibrahim El-Henawy},
  doi       = {10.14569/IJACSA.2017.080656},
  url       = {https://doi.org/10.14569/IJACSA.2017.080656}
}