Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Various approaches to reasoning with frequency based decision reducts: a survey
Rough set methods and applications
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Data reduction: discretization of numerical attributes
Handbook of data mining and knowledge discovery
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Reduct Generation and Classification of Gene Expression Data
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the evaluation of the decision performance of an incomplete decision table
Data & Knowledge Engineering
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Credible rules in incomplete decision system based on descriptors
Knowledge-Based Systems
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
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Gene selection using rough set theory
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Gene selection is to select the most informative genes from the whole gene set, which is a key step of the discriminant analysis of microarray data. Rough set theory is an efficient mathematical tool for further reducing redundancy. The main limitation of traditional rough set theory is the lack of effective methods for dealing with real-valued data. However, gene expression data sets are always continuous. This has been addressed by employing discretization methods, which may result in information loss. This paper investigates one approach combining feature ranking together with features selection based on tolerance rough set theory. Moreover, this paper explores the other method which can utilize the information contained within the boundary region to improve classification accuracy in gene expression data. Compared with gene selection algorithm based on rough set theory, the proposed methods are more effective for selecting high discriminative genes in cancer classification.