Artificial Intelligence Review - Special issue on lazy learning
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
Lazy learning meets the recursive least squares algorithm
Proceedings of the 1998 conference on Advances in neural information processing systems II
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A tree augmented classifier based on Extreme Imprecise Dirichlet Model
International Journal of Approximate Reasoning
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Evaluating credal classifiers by utility-discounted predictive accuracy
International Journal of Approximate Reasoning
Hi-index | 0.00 |
We propose a local (or lazy) version of the naive credal classifier. The latter is an extension of naive Bayes to imprecise probability developed to issue reliable classifications despite small amounts of data, which may then be carrying highly uncertain information about a domain. Reliability is maintained because credal classifiers can issue set-valued classifications on instances that are particularly difficult to classify. We show by extensive experiments that the local classifier outperforms the original one, both in terms of accuracy of classification and because it leads to stronger conclusions (i.e., set-valued classifications made by fewer classes). By comparing the local credal classifier with a local version of naive Bayes, we also show that the former reliably deals with instances which are difficult to classify, unlike the local naive Bayes which leads to fragile classifications.