A Logic Based Feature Selection Method for Improving the Accuracy of Data Mining Classification Algorithms

Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with pertinent features would reduce the size of dataset and afterwards allow more apparent analysis of extracted rules pattern and rules. This paper introduces a method called logic function-based feature selection (L.FB.F.S) for improving the accuracy of data mining classification algorithms. The goal of the feature selection is to find the minimal subsets of attributes of the dataset that can be used for classification tasks by removing both the irrelevant and redundant features. Primarily, L.FB.F.S finds the all MSs of a dataset. Secondly, one of MS with the best classification ability is selected for improving the accuracy of data mining classification algorithms. Key-Words: Feature selection, attribute reduction, classification, Boolean function