Journal
KNOWLEDGE-BASED SYSTEMS
Volume 36, Issue -, Pages 206-216Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2012.06.010
Keywords
Feature selection; Rough set theory; Incomplete decision system; Rough entropy; Conditional entropy
Categories
Funding
- National Natural Science Foundation of China [60873104, 61040037]
- Key Project of Science and Technology Department of Henan Province [112102210194]
- Science and Technology Research Key Project of Educational Department of Henan Province [12A520027]
- Education Fund for Youth Key Teachers of Henan Normal University
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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. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
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