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Title: Using ensemble decisions and active selection to improve low-cost labeling for multi-view data
Authors: Rebbapragada, Umaa
Wagstaff, Kiri L.
Keywords: machine learning
multi-view data
supervised learning
Issue Date: 2-Jul-2011
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2011.
Citation: Proceedings of the 28th International Conference on Machine Learning, Bellevue, Washington, July 2, 2011.
Abstract: This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines highconfidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensornetwork environments.
Appears in Collections:JPL TRS 1992+

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