Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
Wrapper filtering criteria via linear neuron and kernel approaches
Computers in Biology and Medicine
Deriving meaningful rules from gene expression data for classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Expert Systems with Applications: An International Journal
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Correlation-based relevancy and redundancy measures for efficient gene selection
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Identification of Full and Partial Class Relevant Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
BASSUM: A Bayesian semi-supervised method for classification feature selection
Pattern Recognition
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
Artificial Intelligence in Medicine
A novel forward gene selection algorithm for microarray data
Neurocomputing
Hi-index | 3.84 |
Motivation: One problem with discriminant analysis of DNA microarray data is that each sample is represented by quite a large number of genes, and many of them are irrelevant, insignificant or redundant to the discriminant problem at hand. Methods for selecting important genes are, therefore, of much significance in microarray data analysis. In the present study, a new criterion, called LS Bound measure, is proposed to address the gene selection problem. The LS Bound measure is derived from leave-one-out procedure of LS-SVMs (least squares support vector machines), and as the upper bound for leave-one-out classification results it reflects to some extent the generalization performance of gene subsets. Results: We applied this LS Bound measure for gene selection on two benchmark microarray datasets: colon cancer and leukemia. We also compared the LS Bound measure with other evaluation criteria, including the well-known Fisher's ratio and Mahalanobis class separability measure, and other published gene selection algorithms, including Weighting factor and SVM Recursive Feature Elimination. The strength of the LS Bound measure is that it provides gene subsets leading to more accurate classification results than the filter method while its computational complexity is at the level of the filter method. Availability: A companion website can be accessed at http://www.ntu.edu.sg/home5/pg02776030/lsbound/. The website contains: (1) the source code of the gene selection algorithm; (2) the complete set of tables and figures regarding the experimental study; (3) proof of the inequality (9). Contact: ekzmao@ntu.edu.sg