Paper Info
Title
Case based reasoning with bayesian model averaging: an improved method for survival analysis on microarray data
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.
Year
DOI
Venue
2010
10.1007/978-3-642-14274-1_26
ICCBR
Keywords
Field
DocType
feature selection,survival analysis,data mining method,improved performance,promising method,bayesian model averaging,improved method,microarray data,expansible model,cancer gene expression datasets,gene selection,gene expression,hybrid bma-cbr classification,data mining,classification,bioinformatics,curse of dimensionality,case base reasoning,limit set
Data mining,Bayesian inference,Expression (mathematics),Feature selection,Computer science,Curse of dimensionality,Microarray analysis techniques,Artificial intelligence,Gene chip analysis,Case-based reasoning,Cluster analysis,Machine learning
Conference
Volume
ISSN
ISBN
6176
0302-9743
3-642-14273-7
Citations 
PageRank 
References 
3
0.39
8
Authors
2
Name
Order
Citations
PageRank
Isabelle Bichindaritz153255.74
Amalia Annest2251.44