Abstract
Gene expression data combined with clinical data has emerged as an important source for survival analysis. However, gene expression data is characterized with thousands of features/genes but only tens or hundreds of observations. The high-dimensionality and unbalance between features and samples pose big challenges for the classical survival analysis methods. This paper proposes a particle swarm optimization based radial basis function networks (PSO-RBFN) for the survival analysis on gene expression data. Particularly, PSO-RBFN applies a principle component analysis for dimensionality reduction and optimizes the RBF network using PSO. The experimental results on three gene expression datasets indicate that PSO-RBFN is able to improve the predict accuracy compared to the other classical survival analysis methods.
Original language | English |
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Title of host publication | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
DOIs | |
Publication status | Published - 2012 |
Event | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia Duration: 10 Jun 2012 → 15 Jun 2012 |
Publication series
Name | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
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Conference
Conference | 2012 IEEE Congress on Evolutionary Computation, CEC 2012 |
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Country/Territory | Australia |
City | Brisbane, QLD |
Period | 10/06/12 → 15/06/12 |
Bibliographical note
Copyright:Copyright 2012 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science