Applying Particle Swarm Optimization-Based Decision Tree Classifier for Cancer Classification on Gene Expression Data

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/107035

Title:	Applying Particle Swarm Optimization-Based Decision Tree Classifier for Cancer Classification on Gene Expression Data
Authors:	Chen, K.-H.;Wang, K.-J.;Wang, K.-M.;Adrian, A-M.
Keywords:	Cancer classification;Gene expression;Particle swarm optimization;C4.5
Date:	2014-11-01
Issue Date:	2016-08-15
Publisher:	Elsevier BV
Abstract:	Background The application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods. Purpose We propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers. Conclusion Statistical analysis reveals that our proposed method outperforms other popular classifiers, i.e., support vector machine, self-organizing map, back propagation neural network, and C4.5 decision tree, by conducting experiments on 11 gene expression cancer datasets.
Relation:	Applied Soft Computing 24, pp.773-780
DOI:	10.1016/j.asoc.2014.08.032
Appears in Collections:	[Graduate Institute & Department of Business Administration] Journal Article

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