A group incremental feature selection for classification using rough set theory based genetic algorithm

Data Mining is one of the most challenging tasks in a dynamic environment due to rapid growth of data with respect to time. Dimension reduction, the key process of relevant feature selection, is applied prior to extracting interesting patterns or information from large repositories of data. In a dynamic environment, newly generated group of data together with the information extracted from the previous data are analyzed to select the most relevant and important features of the entire data set. As a result, efficiency and acceptability of the incremental feature selection model increase in the field of data mining. In our paper, a group incremental feature selection algorithm is proposed using rough set theory based genetic algorithm for selecting the optimized and relevant feature subset, called reduct. The objective function of the genetic algorithm used for incremental feature selection is defined using the previously generated reduct and positive region of the target set, concepts of rough set theory. The method may be applied in a regular basis in the dynamic environment after small to moderate volume of data being added into the system and thus the computational time, the major issue of the genetic algorithm does not affect the proposed method. Experimental results on benchmark datasets demonstrate that the proposed method provides satisfactory results in terms of number of selected features, computation time and classification accuracies of various classifiers. (c) 2018 Elsevier B.V. All rights reserved.