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Extended Tree Augmented Naive Classifier

  • Cassio P. de Campos
  • Marco Cuccu
  • Giorgio Corani
  • Marco Zaffalon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8754)

Abstract

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

Keywords

Bayesian Network Score Function Directed Acyclic Graph Minimum Span Tree Minimum Span Tree Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cassio P. de Campos
    • 1
  • Marco Cuccu
    • 2
  • Giorgio Corani
    • 1
  • Marco Zaffalon
    • 1
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)Switzerland
  2. 2.Università della Svizzera italiana (USI)Switzerland

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