关键词: 核函数, 主成分分析, 决策树, 旋转森林, 基因数据分类

Abstract: Rotation forest (RoF) algorithm is an ensemble classification algorithm using linear analysis theory and decision trees. The rotation forest achieves higher classification accuracy and superior performance with less number of classifiers. However, the classification accuracy decreases for gene expression data with linearly inseparable cases. To address this issue, this paper proposes a rotation forest algorithm based on kernel principal component analysis (KPCA-RoF), chooses the Gaussian kernel function and principal component analysis to implement the nonlinear mapping and deal with differences in gene data. The proposed algorithm focuses on the optimization of parameters, and uses decision tree algorithm for ensemble learning. Experiments show that the new algorithm well addresses the linearly inseparabal issue and improves the classification accuracy.

Key words: kernel function, principal component analysis, decision tree, rotation forest, gene data classification