On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction
IEEE Transactions on Information Technology in Biomedicine
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Many classification methods are used in microarray gene expression data analysis to identify genes that are predictive to clinical outcomes (survival/fatal) of certain diseases. However, the reliability of these methods is often not well established due to the imprecision of the method and uncertainty of the dataset. In this paper, a knowledge-based belief reasoning system (BRS) is proposed to solve the problem by dealing with the uncertainties inherent in the results of various classification methods. Through the belief combination process, we pursue a means to reduce the uncertainty and improve the reliability of classification so that the underlying features of gene behavior recorded in the microarray expression profiles could be convincingly revealed.