An Associative Classifier for Uncertain Datasets
- Metanat Hooshsadat
- Osmar R. Zaiane, University of Alberta (Database)
The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their promising results on standard datasets. We propose a novel associative classifier for uncertain data. Our method, Uncertain Associative Classifier (UAC) is efficient and has an effective rule pruning strategy. Our experimental results on real datasets show that in most cases, UAC reaches better accuracies than the state of the art algorithms.
Citation
M. Hooshsadat, O. Zaiane. "An Associative Classifier for Uncertain Datasets". Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp 342-353, May 2012.| Keywords: | |
| Category: | In Conference |
BibTeX
@incollection{Hooshsadat+Zaiane:PAKDD12,
author = {Metanat Hooshsadat and Osmar R. Zaiane},
title = {An Associative Classifier for Uncertain Datasets},
Pages = {342-353},
booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD)},
year = 2012,
}Last Updated: January 13, 2020Submitted by Sabina P
