Bagging for Gaussian process regression
Neurocomputing
MRI-based classification of brain tumor type and grade using SVM-RFE
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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IEEE Transactions on Information Technology in Biomedicine
Computers in Biology and Medicine
Improving bagging performance through multi-algorithm ensembles
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Microarray gene expression: A study of between-platform association of Affymetrix and cDNA arrays
Computers in Biology and Medicine
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This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction