Affiliations: School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, China
Correspondence:
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Corresponding author. School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, 723000, China. E-mail: [email protected].
Abstract: In order to improve the classification effect of the gene expression profile with high dimension and small sample, this paper proposes an improved rotation forest algorithm. Firstly, the information index to classification algorithm (IIC) can’t deal with multi-class problems, and then an improved IIC algorithm is proposed to achieve the attribute reduction so that the noise genes are eliminated and the dimensions of feature space are reduced. Secondly, the rotation forest algorithm was improved from the two aspects of improving diversity and accuracy of the base classifiers to classify gene expression profiles. The simulation experimental results on the benchmark gene expression profile datasets show that our proposed algorithm is better than rotation forest algorithm in classification accuracy, stability and running time.
Keywords: Gene expression profiles, classification, the multiclass information index to
classification, rotation forest algorithm, ensemble learning
