Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
An introduction to variable and feature selection
The Journal of Machine Learning Research
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Toward Robust Distance Metric Analysis for Similarity Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A novel ensemble of classifiers for microarray data classification
Applied Soft Computing
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Microarray cancer classification has drawn attention of research community for better clinical diagnosis in last few years Microarray datasets are characterized by high dimension and small sample size To avoid curse of dimensionality good feature selection methods are needed Here, we propose a two stage algorithm for finding a small subset of relevant genes responsible for classification in high dimensional microarray datasets In first stage of algorithm, the entire feature space is divided into k clusters using normalized cut Similarity measure used for clustering is maximal information compression index The informative gene is selected from each cluster using t-statistics and a pool of non redundant genes is created In second stage a wrapper based forward feature selection method is used to obtain a set of optimal genes for a given classifier The proposed algorithm is tested on three well known datasets from Kent Ridge Biomedical Data Repository Comparison with other state of art methods shows that our proposed algorithm is able to achieve better classification accuracy with less number of features.