Abstract
Microarray technology enables the simultaneous measurement of thousands of gene expressions, while often providing a limited set of samples. These datasets require data mining methods for classification, prediction, and clustering to be tailored to the peculiarity of this domain, marked by the so called ‘curse of dimensionality’. One main characteristic of these specialized algorithms is their intensive use of feature selection for improving their performance. One promising method for feature selection is Bayesian Model Averaging (BMA) to find an optimal subset of genes. This article presents BMA applied to gene selection for classification on two cancer gene expression datasets and for survival analysis on two cancer gene expression datasets, and explains how case based reasoning (CBR) can benefit from this model to provide, in a hybrid BMA-CBR classification or survival prediction method, an improved performance and more expansible model.
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Bichindaritz, I., Annest, A. (2010). Case Based Reasoning with Bayesian Model Averaging: An Improved Method for Survival Analysis on Microarray Data. In: Bichindaritz, I., Montani, S. (eds) Case-Based Reasoning. Research and Development. ICCBR 2010. Lecture Notes in Computer Science(), vol 6176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14274-1_26
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DOI: https://doi.org/10.1007/978-3-642-14274-1_26
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