Rough set approach to incomplete information systems
Information Sciences: an International Journal
The algorithm on knowledge reduction in incomplete information systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Valued Tolerance and Decision Rules
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Maximal consistent block technique for rule acquisition in incomplete information systems
Information Sciences: an International Journal
An Efficient Method for Attribute Reduction in Incomplete Information Systems
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Information Sciences: an International Journal
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
Feature selection using rough entropy-based uncertainty measures in incomplete decision systems
Knowledge-Based Systems
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Dataset dimensionality is one of the primary impediments to data analysis in areas such as pattern recognition, data mining, and decision support. A feature subset that possesses the same classification power as that of the whole feature set is expected to be found prior to performing a classification task. For this purpose, many rough set algorithms for feature selection have been developed and applied to incomplete decision systems. When they address large data, however, their undesirable efficiencies could be intolerable. This paper proposes a boundary region-based feature selection algorithm (BRFS), which has the ability to efficiently find a feature subset from a large incomplete decision system. BRFS captures an inconsistent block family to construct a rough set boundary region and designs a positive stepwise mechanism for the construction of boundary regions with respect to multiple attribute subsets, making the acquisition of boundary regions highly efficient. The boundary regions are used to build significance measures as heuristics to determine the optimal search path and establish an evaluation criterion for rules to identify feature subsets. These arrangements make BRFS capable of locating a reduct more efficiently than other available algorithms; this finding is supported by experimental results.