Constraint-based, transductive learning for distributed ensemble classification

David J. Miller, Siddharth Pal, Yue Wang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations

    Abstract

    We consider ensemble classification when there is no common labeled data for designing the function which aggregates classifier decisions. In recent work, we dubbed this problem distributed ensemble classification, addressing e.g. when local classifiers are trained on different (e.g. proprietary, legacy) databases or operate on different sensing modalities. Typically, fixed (untrained) rules of classifier combination such as voting methods are used in this case. However, these may perform poorly, especially when the local class priors, used in training, differ from the true (test batch) priors. Alternatively, we proposed a transductive strategy, optimizing the combining rule for an objective function measured on the test batch. We proposed both maximum likelihood (ML) and information-theoretic (IT) objectives and found that IT achieved superior performance. Here, we identify that the fundamental advantage of the IT method is its ability to properly account for statistical redundancy in the ensemble. We also develop an extension of IT that improves its performance. Experiments are conducted on the UC Irvine machine learning repository.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
    PublisherIEEE Computer Society
    Pages15-20
    Number of pages6
    ISBN (Print)1424406560, 9781424406562
    DOIs
    StatePublished - Jan 1 2006
    Event2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 - Maynooth, Ireland
    Duration: Sep 6 2006Sep 8 2006

    Publication series

    NameProceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006

    Other

    Other2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
    CountryIreland
    CityMaynooth
    Period9/6/069/8/06

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Software
    • Signal Processing

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