Statistical analysis with missing data
Statistical analysis with missing data
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
On the classification performance of TAN and general Bayesian networks
Knowledge-Based Systems
An introduction to the imprecise Dirichlet model for multinomial data
International Journal of Approximate Reasoning
Bayesian networks and the imprecise Dirichlet model applied to recognition problems
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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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.