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Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

Ye, Qiang (2010) Competitive Learning Neural Network Ensemble Weighted by Predicted Performance. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different training signals for base networks in an ensemble can effectively promote diversity and improve ensemble performance. Here a Competitive Learning Neural Network Ensemble is proposed where a secondary output unit predicts the classification performance of the primary output unit in each base network. The networks compete with each other on the basis of classification performance and partition the stimulus space. The secondary units adaptively receive different training signals depending on the competition. As the result, each base network develops ¡°preference¡± over different regions of the stimulus space as indicated by their secondary unit outputs. To form an ensemble decision, all base networks¡¯ primary unit outputs are combined and weighted according to the secondary unit outputs. The effectiveness of the proposed approach is demonstrated with the experiments on one real-world and four artificial classification problems.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMunro, Paulpmunro@sis.pitt.eduPWM
Committee MemberParmanto, Bambangparmanto@pitt.eduPARMANTO
Committee MemberKarimi, Hassanhkarimi@sis.pitt.eduHKARIMI
Committee MemberIyengar, Satishssi@pitt.eduSSI
Committee MemberHirtle, Stephenshirtle@sis.pitt.eduHIRTLE
Date: 12 May 2010
Date Type: Completion
Defense Date: 5 March 2010
Approval Date: 12 May 2010
Submission Date: 10 March 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: bias and variance; classification; diversity; ensemble; multi-task learning; competitive learning; neural networks
Other ID:, etd-03102010-152349
Date Deposited: 10 Nov 2011 19:32
Last Modified: 15 Nov 2016 13:37


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