Pruning Training Sets for Learning of Object Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Feature-based approach to semi-supervised similarity learning
Pattern Recognition
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis
Computers in Biology and Medicine
A hybrid feature selection method for DNA microarray data
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Computers in Biology and Medicine
Simultaneous sample and gene selection using t-score and approximate support vectors
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Gene expression datasets is a means to classify and predict the diagnostic categories of a patient. Informative genes and representative samples selection are two important aspects for reducing gene expression data. Identifying and pruning redundant genes and samples simultaneously can improve the performance of classification and circumvent the local optima problem. In the present paper, the modified particle swarm optimization was applied to selecting optimal genes and samples simultaneously and support vector machine was used as an objective function to determine the optimum set of genes and samples. To evaluate the performance of the new proposed method, it was applied to three publicly available microarray datasets. It has been demonstrated that the proposed method for gene and sample selection is a useful tool for mining high dimension data.