Feature selection for imbalanced data based on neighborhood rough sets

Feature selection is a meaningful aspect of data mining that aims to select more relevant data features and provide more concise and explicit data descriptions. It is beneficial for constructing an effective learning model and reducing the consumption of memory and time. In real-life applications, imbalanced data are ubiquitous, such as those in medical diagnoses, intrusion detection, and credit ratings. In recent years, feature selection for imbalanced data has attracted increasing research attention. Neighborhood rough set theory has been effectively applied to feature selection when dealing with mixed types of data. In this study, we propose an approach for feature selection for imbalanced data employing neighborhood rough set theory. The significance of features is defined by carefully studying the upper and lower boundary regions. The uneven distribution of the classes is considered during the definition of the feature significance. A discernibility-matrix-based feature selection method, which is a key method in rough set theory, is used; then, a novel algorithm for feature selection (RSFSAID) is proposed. The uncertainty of feature selection resulting from different parameters is investigated, and a particle swarm optimization algorithm is used to determine the optimized parameters in the algorithm. Extensive experiments are performed with public datasets to evaluate the proposed method. Experimental results show that the RSFSAID algorithm can improve the classification performance of imbalanced data compared to four other algorithms. (C) 2019 Elsevier Inc. All rights reserved.