title: Tuning diversity in bagged ensembles.
abstract: In this paper, we investigate how the level of diversity amongst individual neural networks in a bagged ensemble can significantly influence overall ensemble generalization performance. We propose a new technique that tunes this diversity so that ensemble generalization performance is optimized and evaluate its performance on benchmark regression data-sets.
authors.affiliation: Department of Computer Science, University of Dublin, Trinity College, Ireland. John.Carney@cs.tcd.ie
authors.forename: J G
authors.initials: JG
authors.lastname: Carney
authors.forename: P
authors.initials: P
authors.lastname: Cunningham
mesh_terms: D000465:Algorithms; D001900:Boston; D019352:Censuses; D003327:Coronary Disease / Q000175:diagnosis / Q000503:physiopathology; D003331:Coronary Vessels / Q000503:physiopathology; D004562:Electrocardiography; D006798:Housing; D006801:Humans; D015141:Los Angeles; D016571:Neural Networks, Computer; D010126:Ozone / Q000032:analysis; D012044:Regression Analysis; D015203:Reproducibility of Results
chemical_list: D010126:Ozone