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Unsupervised Feature Selection Based on the Distribution of Features Attributed to Imbalanced Data Sets
Mina Alibeigi, Sattar Hashemi, Ali Hamzeh
Pages - 14 - 22     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 2   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
Feature, Feature Selection, Filter Approach, Imbalanced Data Set
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions 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, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original 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 our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
CITED BY (10)  
1 Pant, H., & Srivastava, R. MINDEX_IB: A Feature Selection method for Imbalanced Dataset. IONOSPHERE, 34(2), 126-225.
2 Pant, H., & Srivastava, R. a survey on feature selection methods for imbalanced datasets.
3 ORESKI, D., & KLICEK, B. A novel feature selection techniques based on contrast set mining.
4 Jiangsheng Yi, & Wanglian Xi. (2013). Unsupervised feature unbalanced data selection method. Small Computer Systems, 34 (1), 63-66.
5 Jiangsheng Yi, & Wanglian Xi. (2013). Unsupervised feature selection method for imbalanced data. Computer Systems, 34 (1), 63-67.
6 Reyes, J. A., Montes, A., González, J. G., & Pinto, D. E. (2013). Clasificación de roles semánticos usando características sintácticas, semánticas y contextuales. Computación y sistemas, 17(2), 263-272.
7 Jiang, S. Y., & Wang, L. X. (2013). Unsupervised Feature Selection Method for Imbalanced Data. Journal of Chinese Computer Systems, 34(1), 63-67.
8 Reyes, J. A., Montes, A., González, J. G., & Pinto, D. E. (2013). Classifying Case Relations using Syntactic, Semantic and Contextual Features. Computación y Sistemas, 17(2).
9 Asaduzzaman, M., Kabir, A. M. E., Uddin, N., Mollah, A. S., & Nurunnabi, M. A Feature Selection Approach Using Asymmetry.
10 Cuaya, G., Munoz-Meléndez, A., & Morales, E. F. (2011). A minority class feature selection method. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (pp. 417-424). Springer Berlin Heidelberg.
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Miss Mina Alibeigi
University - Iran
Dr. Sattar Hashemi
Shiraz University - Iran
Dr. Ali Hamzeh
Shiraz University - Iran