Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gene extraction for cancer diagnosis by support vector machines-An improvement
Artificial Intelligence in Medicine
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Improving reliability of gene selection from microarray functional genomics data
IEEE Transactions on Information Technology in Biomedicine
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
Evaluation of Feature Selection Measures for Steganalysis
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Partition-conditional ICA for Bayesian classification of microarray data
Expert Systems with Applications: An International Journal
Relevant feature selection from EEG signal for mental task classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
A three phase approach for mental task classification using EEG
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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The development of DNA microarray technology has facilitated in-depth research into cancer classification, and has made it possible to process thousands of genes simultaneously. Since not all genes are crucial for classifying cancer, it is necessary to select informative genes which are associated with cancer. Many gene selection methods have been investigated, but none is perfect. In this paper we investigate methods of finding optimal informative genes for classification of gene expression profiles. We propose a new gene selection method based on the forward selection method with regression analysis in order to find informative genes which predict cancer. The genes selected by this method tend to have information about the cancer that does not overlap with the other genes selected. We have measured the sensitivity, specificity, and recognition rate of the selected genes with the $k$-nearest-neighbour classifier for the colon cancer dataset and the lymphoma dataset. In most cases, the proposed method produces better results than gene selection based on other feature selection methods, yielding a high accuracy of 90.3% for the colon cancer dataset and 72% for the lymphoma dataset.