|Friday, February 21|
|CS03 Ensemble Modeling||
Fri, Feb 21, 9:15 AM - 10:45 AM
Ensemble Learning with Trees and Rules: Supervised, Semi-supervised, Unsupervised (302754)*Deniz Akdemir, Cornell University
Keywords: Ensemble learning, big data, rule ensembles, supervised learning, semisupervised learning, unsupervised learning
Ensemble learning provides solutions to complex statistical prediction problems by simultaneously using a number of models. By bounding false idealizations, focusing on regularities and stable common behavior, ensemble modeling approaches provide solutions that as a whole outperform the single models. In this presentation, I review several approaches for post processing a large ensemble of conjunctive rules for supervised, semi-supervised and unsupervised learning problems. For high dimensional regression and classification problems the models constructed by post processing rules have signi?cantly better prediction performance than some other popular ensemble learning approaches. When rule ensembles are used for semi-supervised and unsupervised learning, the internal and external measures of cluster validity point to high quality groupings.