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An Adaptive Position Synchronization Controller Using Orthogonal Neural Network for 3-DOF Planar Parallel Manipulators Read chapter Read first chapter

2017 | OriginalPaper | Chapter

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

Authors : Huijuan Lu, Yaqiong Meng, Ke Yan, Yu Xue, Zhigang Gao

Published in: Intelligent Computing Methodologies

Publisher: Springer International Publishing

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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.

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Metadata
Title
Classifying Non-linear Gene Expression Data Using a Novel Hybrid Rotation Forest Method
Authors
Huijuan Lu
Yaqiong Meng
Ke Yan
Yu Xue
Zhigang Gao
Copyright Year
2017
Book
Intelligent Computing Methodologies

Print ISBN: 978-3-319-63314-5

Electronic ISBN: 978-3-319-63315-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63315-2_64

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