Dec. 18, 2006 to Dec. 22, 2006
Maria-Luiza Antonie , University of Alberta, Canada
Osmar R. Zaiane , University of Alberta, Canada
Robert C. Holte , University of Alberta, Canada
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2006.97
Association rule-based classifiers have recently emerged as competitive classification systems. However, there are still deficiencies that hinder their performance. One defi- ciency is the use of rules in the classification stage. Current systems assign classes to new objects based on the best rule applied or on some predefined scoring of multiple rules. In this paper we propose a new technique where the system automatically learns how to use the rules. We achieve this by developing a two-stage classification model. First, we use association rule mining to discover classification rules. Second, we employ another learning algorithm to learn how to use these rules in the prediction process. Our two-stage approach outperforms C4.5 and RIPPER on the UCI datasets in our study, and outperforms other rule-learning methods on more than half the datasets. The versatility of our method is also demonstrated by applying it to text classification, where it equals the performance of the best known systems for this task, SVMs.
Maria-Luiza Antonie, Osmar R. Zaiane, Robert C. Holte, "Learning to Use a Learned Model: A Two-Stage Approach to Classification", ICDM, 2006, Sixth International Conference on Data Mining (ICDM'06), Sixth International Conference on Data Mining (ICDM'06) 2006, pp. 33-42, doi:10.1109/ICDM.2006.97