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C-KPCA: Custom Kernel PCA for Cancer Classification

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

Principal component analysis (PCA) is an effective and well-known method for reducing high-dimensional data sets. Recently, KPCA (Kernel PCA), a nonlinear form of PCA, has been introduced into many fields. In this paper, we propose a new gene selection, namely Custom Kernel principal component analysis (C-KPCA). The new kernel function for KPCA is created by combining a set of kernel functions. First, Singular Value Decomposition (SVD) is used to reduce the dimension of microarray data. Input space is then mapped to a higher-dimensional feature space using the proposed custom kernel function. The main objective of our method is to extract nonlinear features for classification process. In order to test the accuracy of our method, a number of experiments are carried out on four binary gene datasets: Colon Tumor, Leukemia, Lymphoma, and Prostate. The experimental results show that our proposed method results in a higher prediction rate as comparing with several recently published algorithms.

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Author information

Authors and Affiliations

  1. Department of Economic Information System, Academy of Finance, Hanoi, Viet Nam

    Van-Sang Ha

  2. Department of Information Technology, VNU-University of Engineering and Technology, Hanoi, Viet Nam

    Ha-Nam Nguyen

Authors
  1. Van-Sang Ha
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  2. Ha-Nam Nguyen
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Corresponding author

Correspondence to Van-Sang Ha .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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© 2016 Springer International Publishing Switzerland

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Ha, VS., Nguyen, HN. (2016). C-KPCA: Custom Kernel PCA for Cancer Classification. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_36

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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