Ensemble Selection for SuperParent-One-Dependence Estimators

@inproceedings{Yang2005EnsembleSF,
  title={Ensemble Selection for SuperParent-One-Dependence Estimators},
  author={Ying Yang and Kevin B. Korb and Kai Ming Ting and Geoffrey I. Webb},
  booktitle={Australian Conference on Artificial Intelligence},
  year={2005}
}
SuperParent-One-Dependence Estimators (SPODEs) loosen Naive-Bayes’ attribute independence assumption by allowing each attribute to depend on a common single attribute (superparent) in addition to the class. An ensemble of SPODEs is able to achieve high classification accuracy with modest computational cost. This paper investigates how to select SPODEs for ensembling. Various popular model selection strategies are presented. Their learning efficacy and efficiency are theoretically analyzed and… CONTINUE READING
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