The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Random Iterative Models
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Leave-One-Out Support Vector Machines
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Gene Selection for Multi-Class Prediction of Microarray Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Does cost-sensitive learning beat sampling for classifying rare classes?
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
A Stochastic Algorithm for Feature Selection in Pattern Recognition
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Using Boosting to prune Double-Bagging ensembles
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
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Microarray technology allows for the monitoring of thousands of gene expressions in various biological conditions, but most of these genes are irrelevant for classifying these conditions. Feature selection is consequently needed to help reduce the dimension of the variable space. Starting from the application of the stochastic meta-algorithm ''Optimal Feature Weighting'' (OFW) for selecting features in various classification problems, focus is made on the multiclass problem that wrapper methods rarely handle. From a computational point of view, one of the main difficulties comes from the unbalanced classes situation that is commonly encountered in microarray data. From a theoretical point of view, very few methods have been developed so far to minimize the classification error made on the minority classes. The OFW approach is developed to handle multiclass problems using CART and one-vs-one SVM classifiers. Comparisons are made with other multiclass selection algorithms such as Random Forests and the filter method F-test on five public microarray data sets with various complexities. Statistical relevancy of the gene selections is assessed by computing the performances and the stability of these different approaches and the results obtained show that the two proposed approaches are competitive and relevant to selecting genes classifying the minority classes. Application to a pig folliculogenesis study follows and a detailed interpretation of the genes that were selected shows that the OFW approach answers the biological question.