Abstract

The aim of this study is to design a Feature Subset Selection Technique that speeds up the Feature Selection (FS) process in high dimensional datasets with reduced computational cost and great efficiency. FS has become the focus of much research on decision support system areas for which data with tremendous number of variables are analyzed. Filters and wrappers are proposed techniques for the feature subset selection process. Filters make use of association based approach but wrappers adopt classification algorithms to identify important features. Filter method lacks the ability of minimization of simplification error while wrapper method burden weighty computational resource. To pull through these difficulties, a hybrid approach is proposed combining both filters and wrappers. Filter approach uses a permutation of ranker search methods and a wrapper which improves the learning accurateness and obtains a lessening in the memory requirements and finishing time. The UCI machine learning repository was chosen to experiment the approach. The classification accuracy resulted from our approach proves to be higher.

Keywords:

Algorithm, filter , machine learning , ranker search, repository, wrapper,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.