A cooperative ensemble learning system

Yong LIU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

45 Citations (Scopus)


This paper presents a new cooperative ensemble learning system (CELS) for designing neural network ensembles. The idea behind CELS is to encourage different individual networks in an ensemble to learn different parts or aspects of the training data so that the ensemble can learn the whole training data better. Rather than producing unbiased individual networks whose errors are uncorrelated, CELS tends to create negatively correlated networks with a novel correlation penalty term in the error function to encourage such specialization. In CELS, individual networks are trained simultaneously rather than sequentially. This provides an opportunity for different networks to cooperate with each other and to specialize. This paper analyzes CELS in terms of bias-variance-covariance trade-off. Experiments on a real-world problem demonstrate that CELS can produce neural network ensembles with good generalization ability.
Original languageEnglish
Title of host publicationThe 1998 IEEE International Joint Conference on Neural Networks Proceedings
Number of pages6
Publication statusPublished - 1998
Externally publishedYes
Event1998 IEEE World Congress on Computational Intelligence - Anchorage, United States
Duration: 4 May 19989 May 1998


Congress1998 IEEE World Congress on Computational Intelligence
Country/TerritoryUnited States


Dive into the research topics of 'A cooperative ensemble learning system'. Together they form a unique fingerprint.

Cite this