Feature selection using rough entropy-based uncertainty measures in incomplete decision systems

  • Authors:

  • Lin Sun;Jiucheng Xu;Yun Tian

  • Affiliations:

  • College of Computer and Information Technology, Henan Normal University, Henan 453007, China and International WIC Institute, Beijing University of Technology, Beijing 100124, China;College of Computer and Information Technology, Henan Normal University, Henan 453007, China;College of Information Science and Technology, Beijing Normal University, Beijing 100875, China

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2012

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Abstract

Feature selection in large, incomplete decision systems is a challenging problem. To avoid exponential computation in exhaustive feature selection methods, many heuristic feature selection algorithms have been presented in rough set theory. However, these algorithms are still time-consuming to compute. It is therefore necessary to investigate effective and efficient heuristic algorithms. In this paper, rough entropy-based uncertainty measures are introduced to evaluate the roughness and accuracy of knowledge. Moreover, some of their properties are derived and the relationships among these measures are established. Furthermore, compared with several representative reducts, the proposed reduction method in incomplete decision systems can provide a mathematical quantitative measure of knowledge uncertainty. Then, a heuristic algorithm with low computational complexity is constructed to improve computational efficiency of feature selection in incomplete decision systems. Experimental results show that the proposed method is indeed efficient, and outperforms other available approaches for feature selection from incomplete and complete data sets.