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International Conference on Future Data and Security Engineering

FDSE 2017: Future Data and Security Engineering pp 139–153Cite as

Parallel Algorithm of Local Support Vector Regression for Large Datasets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10646))

Abstract

We propose the new parallel algorithm of local support vector regression (local SVR), called kSVR for effectively dealing with large datasets. The learning strategy of kSVR performs the regression task with two main steps. The first one is to partition the training data into k clusters, followed which the second one is to learn the SVR model from each cluster to predict the data locally in the parallel way on multi-core computers. The kSVR algorithm is faster than the standard SVR for the non-linear regression of large datasets while maintaining the high correctness in the prediction. The numerical test results on datasets from UCI repository showed that our proposed kSVR is efficient compared to the standard SVR.

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Notes

  1. 1.

    It must be noted that the complexity of the kSVR approach does not include the kmeans clustering used to partition the full dataset. But this step requires insignificant time compared with the quadratic programming solution.

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Author information

Authors and Affiliations

  1. College of Information Technology, Can Tho University, Cantho, 92100, Vietnam

    Le-Diem Bui, Minh-Thu Tran-Nguyen & Thanh-Nghi Do

  2. UMI UMMISCO 209 (IRD/UPMC), UPMC, Sorbonne University, Pierre and Marie Curie University, Paris 6, France

    Thanh-Nghi Do

  3. AI Lab, Computer Science Department, Gyeongsang National University, Jinju, Korea

    Le-Diem Bui & Yong-Gi Kim

Authors
  1. Le-Diem Bui
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  2. Minh-Thu Tran-Nguyen
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  3. Yong-Gi Kim
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  4. Thanh-Nghi Do
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Corresponding author

Correspondence to Thanh-Nghi Do .

Editor information

Editors and Affiliations

  1. HCMC University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University Linz, Linz, Austria

    Roland Wagner

  3. Johannes Kepler University Linz, Linz, Austria

    Josef Küng

  4. Ho Chi Minh City University of Technolog , Ho Chi Minh City, Vietnam

    Nam Thoai

  5. Hosei University, Koganei, Tokyo, Japan

    Makoto Takizawa

  6. University of Vienna, Vienna, Austria

    Erich J. Neuhold

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Bui, LD., Tran-Nguyen, MT., Kim, YG., Do, TN. (2017). Parallel Algorithm of Local Support Vector Regression for Large Datasets. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2017. Lecture Notes in Computer Science(), vol 10646. Springer, Cham. https://doi.org/10.1007/978-3-319-70004-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-70004-5_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70004-5

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