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Importance weighted passive learning

Published: 29 October 2012 Publication History

Abstract

Importance weighted active learning (IWAL) introduces a weighting scheme to measure the importance of each instance for correcting the sampling bias of the probability distributions between training and test datasets. However, the weighting scheme of IWAL involves the distribution of the test data, which can be straightforwardly estimated in active learning by interactively querying users for labels of selected test instances, but difficult for conventional learning where there are no interactions with users, referred as passive learning. In this paper, we investigate the insufficient sampling bias problem, i.e., bias occurs only because of insufficient samples, but the sampling process is unbiased. In doing this, we present two assumptions on the sampling bias, based on which we propose a practical weighting scheme for the empirical loss function in conventional passive learning, and present IWPL, an importance weighted passive learning framework. Furthermore, we provide IWSVM, an importance weighted SVM for validation. Extensive experiments demonstrate significant advantages of IWSVM on benchmarks and synthetic datasets.

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K. Jărvelin and J. Kekălăinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst, 20(4):422--446, 2002.
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M. Li and I. Sethi. Confidence-based classifier design. Patt. Rec., 39(7):1230--1240, 2006.
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B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin--Madison, 2009.
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M. Sugiyama, M. Krauledat, and K. Müller. Covariate shift adaptation by importance weighted cross validation. J. Mach. Learn. Res., 8:985--1005, 2007.

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  1. Importance weighted passive learning

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    cover image ACM Conferences
    CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
    October 2012
    2840 pages
    ISBN:9781450311564
    DOI:10.1145/2396761
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 29 October 2012

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    Author Tags

    1. classification
    2. discounted confidence
    3. learning with confidence

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