A.: Unsupervised Feature Selection Based on the Distribution of Features Attributed to Imbalanced Data Sets

Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reduction methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval and gene expressions among many others. Among feature reduction techniques, feature selection is one of the most popular methods due to the preservation of the original meaning of features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of the proposed method in comparison with other rival methods in terms of both AUC and F1 performance measures of 1-Nearest Neighbor and Naïve Bayes classifiers and the percent of the selected features