Paper Info
Title
AMORI: A Metric-Based One Rule Inducer
Abstract
The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these re- quirements can be expressed as application-specific perfor- mance metrics. An algorithm that is designed to maximize performance given a certain learning metric may not pro- duce the best possible result according to these application- specific metrics. We have implemented A Metric-based One Rule Inducer (AMORI), for which it is possible to select the learning metric. We have compared the performance of this algorithm by embedding three dierent learning met- rics (classification accuracy, the F-measure, and the area under the ROC curve), on 19 UCI data sets. In addition, we have compared the results of AMORI with those ob- tained using an existing rule learning algorithm of similar complexity (One Rule) and a state-of-the-art rule learner (Ripper). The experiments show that a performance gain is achieved, for all included metrics, when using identical met- rics for learning and evaluation. We also show that each AMORI/metric combination outperforms One Rule when using identical learning and evaluation metrics. The perfor- mance of AMORI is acceptable when compared with Ripper. Overall, the results suggest that metric-based learning is a viable approach.
Year
Venue
Keywords
2009
SDM
data mining,roc curve,computer science
Field
DocType
Citations 
Pattern recognition,Computer science,Artificial intelligence,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Niklas Lavesson114821.83
Paul Davidsson231553.19