¹International WIC Institute, Beijing University of Technology, Beijing 100124, P. R. China
²College of Computer & Information Engineering, Henan Normal University, Xinxiang 453007, P. R. China
³Engineering and Technology Research Center for Computational Intelligence and Data Mining of Universities of Henan Province, Xinxiang 453007, P. R. China

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Abstract—Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Considering a consistency measure introduced in rough sets, the problem of feature selection aims to retain the discriminatory power of original features. Many heuristic feature selection algorithms have been proposed, however, these methods are computationally time-consuming. This paper introduces granular space, positive granular space and negative granular space based on granular computing in simplified decision systems, and then new feature significance measure is proposed. Meanwhile, their important propositions and properties are derived. Furthermore, by virtue of radix sorting and Hash techniques, the object granules as basic processing elements are employed to investigate feature selection, and then a heuristic algorithm with low computational complexity is explored. Numerical simulation experiments show that the proposed approach is indeed efficient, and therefore of practical value to many real-world problems.

Index Terms—Granular computing, rough set theory, feature selection, granular space, positive granular space, negative granular space.

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Cite: Lin Sun, Jiucheng Xu, Yuwen Hu, Lina Du, "Granular Space-Based Feature Selection and Its Applications," Journal of Software vol. 8, no. 4, pp. 817-826, 2013.