A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
Analysis of gene expression profiles: class discovery and leaf ordering
Proceedings of the sixth annual international conference on Computational biology
The algorithm on knowledge reduction in incomplete information systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information Sciences—Informatics and Computer Science: An International Journal
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
User-Oriented Feature Selection for Machine Learning
The Computer Journal
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Gene selection using rough set theory
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems
Fuzzy Sets and Systems
An efficient fuzzy rough approach for feature selection
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Information Sciences: an International Journal
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Establishing a classification model for cancer recognition based on DNA microarrays is useful for cancer diagnosis. Feature selection is a key step to perform cancer classification with DNA microarrays, for there is a large number of genes from which to predict classes and a relatively small number of samples. Automatic methods must be developed for extracting relevant genes which are essential for classification. This paper proposes a novel approach for reducing data redundancy based on fuzzy rough set theory and information theory. A mutual information-based algorithm for attribute reduction is suggested. The method is applied to the problem of gene selection for cancer classification. Experimental results show that the algorithm is more effective than conventional rough sets based approaches.