Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
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Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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Transactions on rough sets VIII
Attribute reduction based on granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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Transactions on Rough Sets V
Unsupervised feature selection in digital mammogram image using rough set theory
International Journal of Bioinformatics Research and Applications
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Attribute reduction is an important issue of rough set theory and has already been separately studied in algebra view and information view. However, conceptions of attribute reduction based on the two views are not necessarily equivalent, they are the same only in consistent decision systems. In this paper, we theoretically study the quantitative relation between some basic notions of rough set theory like attribute reduction, attribute significance and attribute core defined in the two views. The results show that the relation between those corresponding conceptions in algebra view and information view is typically inclusion rather than equivalence, and its reason is that information view restricts attributes and systems more specifically than algebra view. The results are necessary and significant for the development and application of attribute reduction methods.