Active Learning with Irrelevant Examples

Autor:	Kiri L. Wagstaff, Dominic Mazzoni, Michael C. Burl
Rok vydání:	2006
Předmět:	Preference learning Computer science business.industry Active systems Semi-supervised learning Machine learning computer.software_genre Support vector machine Probability theory Active learning Data system Artificial intelligence business Classifier (UML) computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540453758 ECML
DOI:	10.1007/11871842_69
Popis:	Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improves the learning rate of three different active learning approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::601869a249594eb726d96940cb923d94 https://doi.org/10.1007/11871842_69 Zobrazit plný text záznamu