An Associative Classifier Based on Positive and Negative Rules
- Maria-Luiza Antonie, Dept. of Computing Science UofA
- Osmar R. Zaiane, University of Alberta (Database)
Associative classifiers use association rules to associate attribute values with observed class labels. This model has been recently introduced in the literature and shows good promise. The proposals so far have only concentrated on, and differ only in the way rules are ranked and selected in the model. We propose a new framework that uses different types of association rules, positive and negative. Negative association rules of interest are rules that either associate negations of attribute values to classes or negatively associate attribute values to classes. In this paper we propose a new algorithm to discover at the same time positive and negative association rules. We introduce a new associative classifier that takes advantage of these two types of rules. Moreover, we present a new way to prune irrelevant classification rules using a correlation coefficient without jeopardizing the accuracy of our associative classifier model. Our preliminary results with UCI datasets are very encouraging.
Citation
M. Antonie, O. Zaiane. "An Associative Classifier Based on Positive and Negative Rules". Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), ACM, pp 64-69, June 2004.| Keywords: | |
| Category: | In Workshop |
| Web Links: | ACM Digital Library |
BibTeX
@misc{Antonie+Zaiane:DMKD04,
author = {Maria-Luiza Antonie and Osmar R. Zaiane},
title = {An Associative Classifier Based on Positive and Negative Rules},
Publisher = "ACM",
Pages = {64-69},
booktitle = {Workshop on Research Issues in Data Mining and Knowledge
Discovery (DMKD)},
year = 2004,
}Last Updated: March 04, 2020Submitted by Sabina P
