It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.
History
Volume
7457
Pagination
147-159
Location
Kuching, Malaysia
Start date
2012-09-05
End date
2012-09-06
ISSN
0302-9743
eISSN
1611-3349
ISBN-13
9783642325403
Language
eng
Publication classification
E Conference publication, E1.1 Full written paper - refereed
Copyright notice
2012, Springer-Verlag Berlin Heidelberg
Editor/Contributor(s)
Richards D, Kang BH
Title of proceedings
PKAW 2012 : Proceedings of the 12th International Workshop on Knowledge Management and Aquisition for Intelligent Systems
Event
Air Force Office of Scientific Research. Conference (12th : 2012 : Kuching, Malaysia)
Publisher
Springer Verlag
Place of publication
Berlin, Germany
Series
Air Force Office of Scientific Research Conference