Enhanced ensemble-based classifier with boosting for pattern recognition

作者：

Highlights：

• Optimization of training sets – irrelevant items elimination.

• Ensembles of neural-networks-based classifiers – a sloppy adaptation.

• Methods of the classifiers diversity enhancing – doubling, shuffling and input filters.

摘要

•Optimization of training sets – irrelevant items elimination.•Ensembles of neural-networks-based classifiers – a sloppy adaptation.•Methods of the classifiers diversity enhancing – doubling, shuffling and input filters.

论文关键词：Neural networks,Classification,Boosting,Ensemble methods,Optimization

论文评审过程：Received 26 January 2017, Revised 30 March 2017, Accepted 12 April 2017, Available online 29 April 2017, Version of Record 29 April 2017.

论文官网地址：https://doi.org/10.1016/j.amc.2017.04.019