期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 16, 期 3, 页码 801-808出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2018.2868341
关键词
Cancer diagnosis; classification; complex networks; DNA microarray analysis; ensemble classifier; gene selection
类别
资金
- Doctoral Foundation of the University of Jinan [XBS1748, XBS1806]
- Shandong Provincial Natural Science Foundation of China [ZR2018LF005]
- National Key Research And Development Program of China [2016YFC0106000]
- Natural Science Foundation of China [61302128]
- Youth Science and Technology Star Program of Jinan City [201406003]
Applications that classify DNA microarray expression data are helpful for diagnosing cancer. Many attempts have been made to analyze these data; however, new methods are needed to obtain better results. In this study, a Complex Network (CN) classifier was exploited to implement the classification task. An algorithm was used to initialize the structure, which allowed input variables to be selected over layered connections and different activation functions for different nodes. Then, a hybrid method integrated the Genetic Programming and the Particle Swarm Optimization algorithms was used to identify an optimal structure with the parameters encoded in the classifier. The single CN classifier and an ensemble of CN classifiers were tested on four bench data sets. To ensure diversity of the ensemble classifiers, we constructed a base classifier using different feature sets, i.e., Pearson's correlation, Spearman's correlation, euclidean distance, Cosine coefficient, and the Fisher-ratio. The experimental results suggest that a single classifier can be used to obtain state-of-the-art results and the ensemble yielded better results.
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