| Резюме: | Due to robustness and efficiency, naive Bayes (NB) remains among the top ten data mining algorithms. However, the required conditional independence assumption more or less limits its classification performance. Of numerous approaches to improving NB, structure extension and instance weighting have both achieved remarkable improvements. To make full use of their complementary and consensus advantages, this article proposes a hybrid modeling approach to combining structure extension with instance weighting. We call the resulting model instance weighted averaged one-dependence estimators (IWAODE). In IWAODE, the dependencies among attributes are modeled by an ensemble of one-dependence estimators, and the corresponding probabilities are estimated from attribute value frequency-weighted training instances. The classification performance of IWAODE is experimentally validated on a large number of datasets.
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