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Medicine Composition Analysis Based on PCA and SVM

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Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3612))

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Abstract

Medicine analysis becomes more and more important in our production and life, especially the composition analysis for medicines. Available data are characterized by small amount and high dimensionality. Support vector machine (SVM) is an ideal algorithm for dealing with this kind of data. This paper presents a combined method of principal component analysis (PCA) and least square support vector machine (LS-SVM) to deal with the work of medicine composition analyses. The proposed method is applied to practical problems. Experiments demonstrate the predominance of the proposed method on both running time and prediction precision.

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Author information

Authors and Affiliations

  1. College of Computer Science, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, 130012, China

    Chaoyong Wang, Chunguo Wu & Yanchun Liang

Authors
  1. Chaoyong Wang
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  2. Chunguo Wu
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  3. Yanchun Liang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, C., Wu, C., Liang, Y. (2005). Medicine Composition Analysis Based on PCA and SVM. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_155

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  • DOI: https://doi.org/10.1007/11539902_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28320-1

  • Online ISBN: 978-3-540-31863-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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