title: Semi-supervised learning of class balance under class-prior change by distribution matching.
abstract: In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning the class ratio of the test dataset is challenging when no labeled data is available from the test domain. In this paper, we propose to estimate the class ratio in the test dataset by matching probability distributions of training and test input data. We demonstrate the utility of the proposed approach through experiments.
authors.affiliation: Tokyo Institute of Technology, 2-12-1-W8-74, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan. Electronic address: christo@sg.cs.titech.ac.jp.
authors.forename: Marthinus Christoffel
authors.initials: MC
authors.lastname: du Plessis
authors.affiliation: Tokyo Institute of Technology, 2-12-1-W8-74, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan. Electronic address: sugi@cs.titech.ac.jp.
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
mesh_terms: D000465:Algorithms; D002965:Classification; D003198:Computer Simulation; D057225:Data Mining; D006801:Humans; D007858:Learning / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011336:Probability
keywords: -divergence; Class-prior change; Density ratio; Selection bias