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

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Intelligent Computing Methodologies (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10363))

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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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Authors and Affiliations

  1. College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Huijuan Lu, Yaqiong Meng & Ke Yan

  2. Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Yu Xue

  3. College of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zhigang Gao

Authors
  1. Huijuan Lu
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  2. Yaqiong Meng
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  3. Ke Yan
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  4. Yu Xue
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  5. Zhigang Gao
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Corresponding author

Correspondence to Ke Yan .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

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Lu, H., Meng, Y., Yan, K., Xue, Y., Gao, Z. (2017). Classifying Non-linear Gene Expression Data Using a Novel Hybrid Rotation Forest Method. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_64

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63314-5

  • Online ISBN: 978-3-319-63315-2

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