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Feature Selection (FS) method is one of the most important data pre-processing steps in data mining domain, it is used to find the essential features subset in order to make a new subset of informative features. The model that used the informative subset such that a classification model built only with this subset would get better predictive accuracy than the model that used a complete set of features. In this paper, we provide a summary of almost all of the methods present in the literature ofdoi:10.30534/ijsait/2019/098620198 fatcat:yhu7zyofljgelb3ocaxy2kcd2a