广西师范大学学报（自然科学版）2006，Vol.24Issue(4)：54-57,4.
异构集成学习中的观察学习机制研究
Observational Learning Algorithm for Heterogeneous Ensembles
摘要
Abstract
Ensemble method has shown the potential to increase classification accuracy beyond the level reached by an individual classifier alone.Observational Learning Algorithm (OLA) is an ensemble method based on social learning theory.Previous work mainly focused on OLA for homogeneous ensembles,such as neural networks ensembles.In this paper,OLA for heterogeneous ensembles was proposed,which is a process with three steps:training,observing,and retraining.Experiments on five datasets from the UCI repository show that,OLA outperforms the individual base learner and majority voting when base learners are not capable enough for the given task.Bias-variance decomposition of the error indicates that OLA can reduce both bias and variance.关键词
观察学习/社会学习理论/分类器集成/异构集成Key words
observational learning/social learning theory/classifiers ensemble/heterogeneous ensemble分类
信息技术与安全科学引用本文复制引用
虞凡,杨利英,覃征..异构集成学习中的观察学习机制研究[J].广西师范大学学报（自然科学版）,2006,24(4):54-57,4.基金项目
Major State Basic Research Development Program of China (973 Program) (2004CB719401) （973 Program）
