| 摘要 |
In a computerized hybrid modeling method and a computer program product for implementing the method, two classification techniques are integrated: expert elicited Bayesian networks and decision trees induced from data. Bayesian networks are a compact representation for probabilistic models and inference. They have been used successfully for many applications involving classification. The tree-based classifiers, on the other hand, have proven their ability to perform well in real world data under uncertainty. For classification purposes, the inference algorithms to compute the exact posterior probability of a target node, given observed evidence in a Bayesian network, are usually computationally intensive or impossible in a mixed model. In those cases, either the approximate results are computed using stochastic simulation methods or the model is approximated using discretization or Gaussian mixture before applying an exact inference algorithm. For a tree-based classifier, however, once the tree is constructed, the classification process is trivial. The hybrid approach synergistically combines the strengths of the two techniques. Such an approach trades off the accuracy and computation. Significant computational savings can be achieved with a minimum classification accuracy drop.
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