集成多层感知器分类器的嵌入式特征排序

Embedded feature ranking for ensemble MLP classifiers.

作者信息

Windeatt Terry, Duangsoithong Rakkrit, Smith Raymond

机构信息

Centre for Vision Speech and Signal Processing, Faculty of Electronics and Physical Sciences, University of Surrey, Guildford Surrey, UK.

出版信息

IEEE Trans Neural Netw. 2011 Jun;22(6):988-94. doi: 10.1109/TNN.2011.2138158. Epub 2011 May 19.

DOI:10.1109/TNN.2011.2138158

PMID:21606023

Abstract

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

摘要

提出了一种用于多层感知器（MLP）集成的特征排序方案，以及基于袋外估计的停止准则。为了解决多类问题，将特征排序与改进的纠错输出编码相结合。在基准数据上的实验结果证明了MLP基分类器在去除无关特征方面的通用性。