title: Generating highly accurate prediction hypotheses through collaborative ensemble learning.
abstract: Ensemble generation is a natural and convenient way of achieving better generalization performance of learning algorithms by gathering their predictive capabilities. Here, we nurture the idea of ensemble-based learning by combining bagging and boosting for the purpose of binary classification. Since the former improves stability through variance reduction, while the latter ameliorates overfitting, the outcome of a multi-model that combines both strives toward a comprehensive net-balancing of the bias-variance trade-off. To further improve this, we alter the bagged-boosting scheme by introducing collaboration between the multi-model's constituent learners at various levels. This novel stability-guided classification scheme is delivered in two flavours: during or after the boosting process. Applied among a crowd of Gentle Boost ensembles, the ability of the two suggested algorithms to generalize is inspected by comparing them against Subbagging and Gentle Boost on various real-world datasets. In both cases, our models obtained a 40% generalization error decrease. But their true ability to capture details in data was revealed through their application for protein detection in texture analysis of gel electrophoresis images. They achieve improved performance of approximately 0.9773 AUROC when compared to the AUROC of 0.9574 obtained by an SVM based on recursive feature elimination.
authors.affiliation: Macedonian Academy of Sciences and Arts, Research Center for Computer Science and Information Technologies, Skopje, 1000, Republic of Macedonia.
authors.forename: Nino
authors.initials: N
authors.lastname: Arsov
authors.affiliation: Macedonian Academy of Sciences and Arts, Research Center for Computer Science and Information Technologies, Skopje, 1000, Republic of Macedonia.
authors.forename: Martin
authors.initials: M
authors.lastname: Pavlovski
authors.affiliation: Macedonian Academy of Sciences and Arts, Research Center for Computer Science and Information Technologies, Skopje, 1000, Republic of Macedonia.|Ss. Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, 1000, Republic of Macedonia.
authors.forename: Lasko
authors.initials: L
authors.lastname: Basnarkov
authors.affiliation: Macedonian Academy of Sciences and Arts, Research Center for Computer Science and Information Technologies, Skopje, 1000, Republic of Macedonia.|Ss. Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, 1000, Republic of Macedonia.|University of California, San Diego, BioCircuits Institute, 9500 Gilman Dr, La Jolla, CA 92093, USA.
authors.forename: Ljupco
authors.initials: L
authors.lastname: Kocarev
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D000069550:Machine Learning; D008962:Models, Theoretical; D009716:Numerical Analysis, Computer-Assisted; D012372:ROC Curve