Rough sets approach to symbolic value partition
International Journal of Approximate Reasoning
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
A Weighted Rough Set Approach for Cost-Sensitive Learning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Weighted rough set learning: towards a subjective approach
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Minimal attribute space bias for attribute reduction
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Research on rough set theory and applications in China
Transactions on rough sets VIII
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Rehabilitation and reconstruction of asphalts pavement decision making based on rough set theory
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
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As a classical problem of Rough Sets, the reduct problem still attracts research interests recently. Many existing works on the reduct problem are devoted into finding optimal reducts where the optimal metrics is the number of attributes. In reality, however, this optimal metrics is not fair since some attributes may have much larger domains than others, and they tend to have better discernibility thus more likely to be included in optimal reducts. To cope with this fairness problem, in this paper we propose the concept of average discernibility which takes into consideration the cardinality of the attribute domain. Attribute reduction based on average discernibility can be implemented through assigning each attribute an appropriate weight in the reduction process to adjust attribute significance. We point out further that some human experts knowledge can also be expressed by the weight vector formed by weights of all attributes. Then we propose a weighted reduction algorithm based on discernibility, and analyze the usefulness the weight vector along with its setting policies. This algorithm is consistent with the existing reduction algorithm based on discernibility in that the former contains the latter as a special case when all elements of the weight vector are equal and non-zero. Experiment results of the Bridges dataset in the UCI library validate the usefulness of our algorithm.