计算机工程与应用2017，Vol.53Issue(8)：57-60,67,5.DOI:10.3778/j.issn.1002-8331.1510-0051
基于差分进化的ELM加权集成分类
ELM weighting ensemble classification based on differential evolution
海宇娇 1刘青昆1
作者信息
- 1. 辽宁师范大学 计算机与信息技术学院,辽宁 大连 116081
- 折叠
摘要
Abstract
Ensemble classification can effectively improve the classification performance by combining several weak classifiers according to a certain rule. In the process of combination, the importance of each weak classifier to the classifi-cation result is often different. Extreme learning machine is a new learning algorithm for training single-hidden-layer feed-forward neural networks. In this paper, the extreme learning machine is selected as a base classifier, and a weighted ensemble method based on differential evolution is proposed. The proposed method optimizes the weights of each base classifier in the ensemble method based on differential evolution algorithm. Experimental results show that the proposed method has higher classification accuracy and better generalization ability compared with the simple-voting method and Adaboost ensemble method.关键词
差分进化/极限学习机/集成分类/加权Key words
differential evolution/extreme learning machine/ensemble classification/weighting分类
信息技术与安全科学引用本文复制引用
海宇娇,刘青昆..基于差分进化的ELM加权集成分类[J].计算机工程与应用,2017,53(8):57-60,67,5.
