title: A Generalized Mixture Framework for Multi-label Classification.
abstract: We develop a novel probabilistic ensemble framework for multi-label classification that is based on themixtures-of-expertsarchitecture. In this framework, we combine multi-label classification models in theclassifier chains familythat decompose the class posterior distributionP(Y1, …,Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input-output and output-output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.
authors.affiliation: Department of Computer Science, University of Pittsburgh.
authors.forename: Charmgil
authors.initials: C
authors.lastname: Hong
authors.affiliation: GE Global Research.
authors.forename: Iyad
authors.initials: I
authors.lastname: Batal
authors.affiliation: Department of Computer Science, University of Pittsburgh.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
keywords: Mixtures-of-experts; Multi-label classification