Classifying Non-linear Gene Expression Data Using a Novel Hybrid Rotation Forest Method

Abstract

Rotation forest (RoF) is an ensemble classifier based on the combination of linear analysis theories and decision tree algorithms. In existing works, the RoF has demonstrated high classification accuracy and good performance with a reasonable number of base classifiers. However, the classification accuracy drops drastically for linearly inseparable datasets. This paper presents a hybrid algorithm integrating kernel principal component analysis and RoF algorithm (KPCA-RoF) to solve the classification problem in linearly inseparable cases. We choose the radial basis function (RBF) kernel for the PCA algorithm to establish the nonlinear mapping and segmentation for gene data. Moreover, we focus on the determination of suitable parameters in the kernel functions for better performance. Experimental results show that our algorithm solves linearly inseparable problem and improves the classification accuracy.