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TRUST-TECH Assisted GA-SVM Ensembles and Its Applications

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Neural Computing for Advanced Applications (NCAA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1637))

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Abstract

A framework of Genetic Algorithm-Support Vector Machine (GA-SVM) is proposed for SVM parameters (model) selection, and clustering algorithm is also integrated with the framework to generate multiple optimal models, as well as being condition of convergence for GA. Moreover, an ensemble method on various SVM models assisted by TRUST-TECH methodology is put forward, to enhance the generalization ability of a single SVM model. The performance of GA-SVM and ensemble method is testified by applying them in both classification and regression problems. Results show that, comparing with traditional parameters selection method (such as grid search), the proposed GA-SVM framework and ensemble strategy can solve general classification and regression issues more efficiently and automatically with better performance.

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Author information

Authors and Affiliations

  1. University of Jinan, Jinan, 250022, SD, China

    Yong-Feng Zhang & Xiao Zhang

  2. Cornell University, Ithaca, NY, 14853, USA

    Hsiao-Dong Chiang

  3. State Grid Baoding Electric Power Supply Company, Baoding, 071000, HB, China

    Yun-Fei Qu

Authors
  1. Yong-Feng Zhang
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  2. Hsiao-Dong Chiang
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  3. Yun-Fei Qu
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  4. Xiao Zhang
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Corresponding author

Correspondence to Yong-Feng Zhang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. University of Jinan, Jinan, China

    Yuehui Chen

  3. Shenzhen University, Shenzhen, China

    Xianghua Chu

  4. Hefei University of Technology, Hefei, China

    Zhao Zhang

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

  6. Chongqing University, Chongqing, China

    Zhou Wu

  7. Western University, London, ON, Canada

    Yimin Yang

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, YF., Chiang, HD., Qu, YF., Zhang, X. (2022). TRUST-TECH Assisted GA-SVM Ensembles and Its Applications. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_8

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  • DOI: https://doi.org/10.1007/978-981-19-6142-7_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6141-0

  • Online ISBN: 978-981-19-6142-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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