Author Ye, Qiang
Title Competitive learning neural network ensemble weighted by predicted performance
book jacket
Descript 88 p
Note Source: Dissertation Abstracts International, Volume: 71-08, Section: A, page: 2690
Adviser: Paul Munro
Thesis (Ph.D.)--University of Pittsburgh, 2010
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
Keywords: ensemble, diversity, neural networks, competitive learning, multi-task learning, bias and variance, classification
School code: 0178
Host Item Dissertation Abstracts International 71-08A
Subject Information Science
Artificial Intelligence
Computer Science
Alt Author University of Pittsburgh