Probabilistic SVM classifier ensemble selection based on GMDH-type neural network

作者：

Highlights：

• We propose a standardized symmetric regularity criterion.

• We define a novel structure of initial model of GMDH.

• We use probabilistic SVM as base learner and integrate the SVM with GMDH.

• We design a special classifier ensemble selection approach named GMDH-PSVM.

• Our method may obtain good classification results.

摘要

•We propose a standardized symmetric regularity criterion.•We define a novel structure of initial model of GMDH.•We use probabilistic SVM as base learner and integrate the SVM with GMDH.•We design a special classifier ensemble selection approach named GMDH-PSVM.•Our method may obtain good classification results.

论文关键词：Probabilistic SVM,Group method of data handling,Ensemble selection,Regularity criterion,Borda sorting

论文评审过程：Received 1 November 2018, Revised 12 October 2019, Accepted 10 April 2020, Available online 15 May 2020, Version of Record 15 May 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107373