Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
Bioinformatics—an introduction for computer scientists
ACM Computing Surveys (CSUR)
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Applied Survival Analysis: Regression Modeling of Time to Event Data
Applied Survival Analysis: Regression Modeling of Time to Event Data
Comparison of reuse strategies for case-based classification in bioinformatics
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Flexible case-based retrieval for comparative genomics
Applied Intelligence
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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.