Change search
ReferencesLink to record
Permanent link

Direct link
AMORI: A Metric-based One Rule Inducer
Responsible organisation
2009 (English)Conference paper (Refereed) Published
Abstract [en]

The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performance metrics. An algorithm that is designed to maximize performance given a certain learning metric may not produce 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 different learning metrics (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 obtained 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 metrics for learning and evaluation. We also show that each AMORI/metric combination outperforms One Rule when using identical learning and evaluation metrics. The performance of AMORI is acceptable when compared with Ripper. Overall, the results suggest that metric-based learning is a viable approach.

Place, publisher, year, edition, pages
SIAM Press , 2009.
Keyword [en]
evaluation, metric, supervised learning, rule learner, Ripper
National Category
Computer Science
URN: urn:nbn:se:bth-8232Local ID: 978-0-898716-82-5OAI: diva2:835921
9th SIAM International Conference on Data Mining
Available from: 2012-09-18 Created: 2009-05-02 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(423 kB)17 downloads
File information
File name FULLTEXT01.pdfFile size 423 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Lavesson, Niklas
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 17 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 24 hits
ReferencesLink to record
Permanent link

Direct link