Corani, Giorgio and De Campos, Cassio Polpo (2010) A tree augmented classifier based on extreme imprecise Dirichlet model. International Journal of Approximate Reasoning, 51 (9). pp. 1053-1068. ISSN 0888-613X
|
Text
1-s2.0-S0888613X10001027-main.pdf - Published Version Download (805kB) | Preview |
Abstract
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.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
|---|---|
| Subjects: | Mathematical sciences > Statistics > Statistical modelling Computer sciences > Artificial intelligence > Machine learning |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull'intelligenza artificiale |
| Depositing User: | Giorgio Corani |
| Date Deposited: | 12 May 2014 09:31 |
| Last Modified: | 06 Jun 2016 06:47 |
| URI: | http://repository.supsi.ch/id/eprint/4724 |
Actions (login required)
 |
View Item |