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There are two main approaches for feature subset selection, i.e., wrapper and filter based. In wrapper based approach, which is a supervised method, the feature subset selection algorithm acts as a wrapper around an induction algorithm. The induction algorithm is actually a black-box for the feature subset selection algorithm and is mostly the classifier itself. The filter approach is an unsupervised method and attempts to assess the merits of features from the data while ignoring the performance of the induction algorithm. In this study, the effects of the feature subset selection approaches on the classification performance of rule-based learning algorithms, i.e., C4.5, RIPPER, PART, BFTree were investigated. These algorithms are fast in case of wrapper based approach. For various datasets, significant accuracy improvements were achieved with the wrapper based feature subset selection method. Other algorithms like Multilayer Perceptron (MLP) and Random Forests (RF) were also applied on the same datasets for the purpose of accuracy comparison. These two algorithms were very inefficient in terms of time when they were used in wrapper approach.