A tree augmented classifier based on Extreme Imprecise Dirichlet Model

G. Corani, C.P. de Campos

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)


We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Original languageEnglish
Pages (from-to)1053-1068
Number of pages16
JournalInternational Journal of Approximate Reasoning
Issue number9
Publication statusPublished - 2010
Externally publishedYes


  • Classification
  • Classifier
  • Imprecise Dirichlet Model
  • Naive credal
  • TANC


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