Some analytical results on critic-driven ensemble classification

David J. Miller, Lian Yan

    Research output: Contribution to conferencePaperpeer-review


    In recent work [14], we proposed a framework for ensemble classification wherein auxiliary networks, dubbed critics, are used to provide reliability information on the ensemble's individual classifiers/experts. We showed experimentally that critic-driven combining schemes extend the applicability of ensemble methods by overcoming the usual requirement that the individual classifier error rate p must be less than 0.5. Here, we support our previous work by proving, under an independence assumption, that performance for a particular critic-driven voting scheme improves with increasing ensemble size N, so long as p + q < 1, with q the critic's error rate in predicting accuracy of expert decisions. While this independence analysis gives significant insight into the conditions for success of critic-based schemes, it does not accurately predict the ensemble performance curve. We thus also develop an analytical approach for predicting the curve, by modeling dependence between experts.

    Original languageEnglish (US)
    Number of pages10
    StatePublished - 1999
    EventProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
    Duration: Aug 23 1999Aug 25 1999


    OtherProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
    CityMadison, WI, USA

    All Science Journal Classification (ASJC) codes

    • Signal Processing
    • Software
    • Electrical and Electronic Engineering


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