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Soft Computing

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12.02.2016 | Methodologies and Application

A novel hybrid classification framework using SVM and differential evolution

verfasst von: Xiaobing Yu, Xuming Wang

Erschienen in: Soft Computing | Ausgabe 14/2017

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Abstract

The kernel parameters setting for SVM in a training process impacts on the classification accuracy. The hybrid framework based on SVM and ensemble differential evolution is proposed to enhance the classification accuracy. The ensemble differential evolution is utilized to optimize the kernel parameters. The ensemble differential evolution algorithm employs two trial vector generation strategies and two control parameter settings. Ten real-world datasets using the proposed hybrid framework are tested. Compared with the SVM variants, the proposed hybrid model improves the classification accuracy of SVM.

Vorheriger Artikel A reproducing kernel method for solving nonlocal fractional boundary value problems with uncertainty

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Titel: A novel hybrid classification framework using SVM and differential evolution
verfasst von: Xiaobing Yu
Xuming Wang
Publikationsdatum: 12.02.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 14/2017
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2054-9

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