Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Grey system and grey relational model
ACM SIGICE Bulletin
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Handling gene redundancy in microarray data using Grey Relational Analysis
International Journal of Data Mining and Bioinformatics
An effective gene selection method based on relevance analysis and discernibility matrix
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Gene selection is a common task in microarray data classification. The most commonly used gene selection approaches are based on gene ranking, in which each gene is evaluated individually and assigned a discriminative score reflecting its correlation with the class according to certain criteria, genes are then ranked by their scores and top ranked ones are selected. Various discriminative scores have been proposed, including t-test, S2N,RelifF, Symmetrical Uncertainty andχ2-statistic. Among these methods, some require abundant data and require the data follow certain distribution, some require discrete data value. In this work, we propose a gene ranking method based on Grey Relational Analysis (GRA) in grey system theory, which requires less data, does not rely on data distribution and is more applicable to numerical data value. We experimentally compare our GRA method with several traditional methods, including Symmetrical Uncertainty, χ2-statistic and ReliefF. The results show that the performance of our method is comparable with other methods, especially it is much faster than other methods.