[1]JI Rui-rui,LIU Ding.Improved gene expression data clustering using a support vector domain description algorithm[J].CAAI Transactions on Intelligent Systems,2009,4(6):544-548.[doi:10.3969/j.issn.1673-4785.2009.06.013]
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Improved gene expression data clustering using a support vector domain description algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 4 Number of periods: 2009 6 Page number: 544-548 Column: 学术论文—机器学习 Public date: 2009-12-25
Title:
Improved gene expression data clustering using a support vector domain description algorithm
Author(s):
JI Rui-rui;  LIU Ding
Center of Information and Control Engineering, Xi’an University of Technology, Xi’an 710048, China
Keywords:
gene expression data;  SVDD;  clustering;  simulated annealing
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.2009.06.013
Abstract:
The application of the support vector domain description (SVDD) algorithm in gene expression data clustering was proposed as a means to improve the low accuracy of current clustering methods. This method effectivly clustered the dataset by finding the optimal separating hyper-sphere. Interclass information was introduced into the current clustering assessment criterion in the form of a minimum within-class distance. The simulated annealing (SA) algorithm was used to find the optimal kernel function parameter and the punishment factor of the SVDD algorithm. Non-sample data were added in training to increase computational efficiency. Simulation results using the yeast cell cycle expression dataset showed that optimal parameters can be obtained faster and more accurately with the new assessment criteria. Similar improvements were found in clustering accuracy and speed.
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