Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

作者：Chen Kun Huang; Wang Kung Jeng^*; Wang Kung Min; Angelia Melani Adrian

来源：Applied Soft Computing, 2014, 24: 773-780.

DOI：10.1016/j.asoc.2014.08.032

• Cancer classification
• Gene expression
• Particle swarm optimization
• C4.5

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.

• 出版日期2014-11