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GA-MKB:A Multi-kernel Boosting Learning Method based on Normalized Kernel Target Alignment and Kernel Difference

Journal of physics. Conference series, 2022-06, Vol.2281 (1), p.12012 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2281/1/012012

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  • Title:
    GA-MKB:A Multi-kernel Boosting Learning Method based on Normalized Kernel Target Alignment and Kernel Difference
  • Author: Chen, Linlin ; Wang, Mei ; Zhang, Qiang ; Hou, Nan
  • Subjects: Algorithms ; Alignment ; Classifiers ; Elections ; Kernel functions ; Physics
  • Is Part Of: Journal of physics. Conference series, 2022-06, Vol.2281 (1), p.12012
  • Description: Abstract Concentrates on the problem that the traditional kernel target alignment(KTA) is not invariance under data translation in the feature space, a cosine matrix alignment method is proposed for kernel selection, which is called normalized kernel target alignment(NKTA). On the basis of normalized kernel target alignment and kernel difference, we propose a new multi-kernel boosting. Firstly, the value of NKTA is taken as the election rarget of the kernel function in each iteration of algorithm, which leads to a selective kernel fusion. Secondly, the kernel difference measure is used to construct the combination coefficient to increase the diversity of weak classifiers, and then improve the generalization performance of integrated strong classifiers. Finally, among the 6 data sets, the GA-MKB performed better than MKBoost-D1 under the accuracy of classification, and can improve the generalization performance of the integrated classifier compared with MKBoost-D2.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2281/1/012012
  • Source: Open Access: IOP Publishing Free Content
    Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    ProQuest Central
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