Hybrid fuzzy multiple SVM classifier through feature fusion based on convolution neural networks and its practical applications

作者：

Highlights：

• The convolution-base and composite kernel are used for alleviating overfitting.

• The composite kernel could adjust the nonlinear fitting ability of the classifier.

• Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion.

• The effectiveness of the HFM-SVM is demonstrated by three practical applications.

摘要

•The convolution-base and composite kernel are used for alleviating overfitting.•The composite kernel could adjust the nonlinear fitting ability of the classifier.•Hybrid fuzzy multi-(HFM) SVM leads to better results with flexible feature fusion.•The effectiveness of the HFM-SVM is demonstrated by three practical applications.

论文关键词：SVM classifier,Composite kernel function,Face Recognition dataset,Black plastic wastes sorting,Partial discharge dataset

论文评审过程：Received 1 November 2021, Revised 24 January 2022, Accepted 25 April 2022, Available online 27 April 2022, Version of Record 30 April 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117392