Cost-sensitive classifier evaluation using cost curves
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Associative classification in the prediction of tuberculosis
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Classifying microarray data with association rules
Proceedings of the 2011 ACM Symposium on Applied Computing
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Biologically relevant association rules for classification of microarray data
ACM SIGAPP Applied Computing Review
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
An associative classifier for uncertain datasets
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
| Hi-index | 0.00 |
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.