Efficient gene selection with rough sets from gene expression data
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
A heuristic algorithm based on attribute importance for feature selection
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Gene selection and cancer classification: a rough sets based approach
Transactions on rough sets XII
Classification by multiple reducts-kNN with confidence
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
The Knowledge Engineering Review
Evolutionary tolerance-based gene selection in gene expression data
Transactions on rough sets XIV
Control of variables in reducts - kNN classification with confidence
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
Modified reducts and their processing for nearest neighbor classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Identification of gene subsets responsible for discerning between available samples of gene microarray data is an important task in Bioinformatics. Due to the large number of genes in samples, there is an exponentially large search space of solutions. The main challenge is to reduce or remove the redundant genes, without affecting discernibility between objects. Reducts, from rough set theory, correspond to a minimal subset of essential genes. We present an algorithm for generating reducts from gene microarray data. It proceeds by preprocessing gene expression data, discretization of real value attributes into categorical followed by positive region based approach for reduct generation. For comparison, different approaches for reduct generation have also been discussed. Results on benchmark gene expression datasets demonstrate more than 90% reduction of redundant genes.