Model Averaging with Discrete Bayesian Network Classifiers

Model Averaging with Discrete Bayesian Network Classifiers

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by Denver Dash , Gregory F. Cooper

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BibTeX

@MISC{Dash_modelaveraging,
    author = {Denver Dash and Gregory F. Cooper},
    title = {Model Averaging with Discrete Bayesian Network Classifiers},
    year = {}
}

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Abstract

This paper considers the problem of performing classification by model-averaging over a class of discrete Bayesian network structures consistent with a partial ordering and with bounded in-degree k. We show that for N nodes this class contains in the worst-case at least Ω ( � �N/2 N/2 k) distinct network structures, but we show that this summation can be performed in O ( � � N k · N) time. We use this fact to show that it is possible to efficiently construct a single directed acyclic graph (DAG) whose predictions approximate those of exact model-averaging over this class, allowing approximate model-averaged predictions to be performed in O(N) time. We evaluate the procedure in a supervised classification context, and show empirically that this technique can be beneficial for classification even when the generating distribution is not a member of the class being averaged over, and we characterize the performance over several parameters on simulated and real-world data.

Keyphrases

discrete bayesian network classifier several parameter partial ordering approximate model-averaged prediction real-world data discrete bayesian network structure bounded in-degree distinct network structure supervised classification context exact model-averaging acyclic graph