Difference between revisions of "Publications:An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier"
From ISLAB/CAISR
(Created page with "<div style='display: none'> == Do not edit this section == </div> {{PublicationSetupTemplate|Author=Jerzy Stefanowski, Sławomir Nowaczyk |PID=587676 |Name=Stefanowski, Jerzy ...") |
|||
Line 4: | Line 4: | ||
{{PublicationSetupTemplate|Author=Jerzy Stefanowski, Sławomir Nowaczyk | {{PublicationSetupTemplate|Author=Jerzy Stefanowski, Sławomir Nowaczyk | ||
|PID=587676 | |PID=587676 | ||
− | |Name=Stefanowski, Jerzy (Poznan University of Technology);Nowaczyk, Sławomir | + | |Name=Stefanowski, Jerzy (Poznan University of Technology);Nowaczyk, Sławomir (slanow) (0000-0002-7796-5201) (Lund University) |
|Title=An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier | |Title=An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier | ||
|PublicationType=Journal Paper | |PublicationType=Journal Paper |
Latest revision as of 21:40, 30 September 2016
Title | An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier |
---|---|
Author | Jerzy Stefanowski and Sławomir Nowaczyk |
Year | 2007 |
PublicationType | Journal Paper |
Journal | International Journal of Computational Intelligence Research |
HostPublication | |
Conference | |
DOI | |
Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:587676 |
Abstract | Multiple classifiers consist of sets of subclassifiers, whose individual predictions are combined to classify new objects. These approaches attract an interest of researchers as they can outperform single classifiers on wide range of classification problems. This paper presents an experimental study of using the rule induction algorithm MODLEM in the multiple classifier scheme called combiner, which is a specific meta learning approach to aggregate answers of component classifiers. Our experimental results show that the improvement of predictive accuracy depends on the independence of errors made by the base classifiers. Moreover, we summarise our experience with using MODLEM as component in other multiple classifiers, namely bagging and n2 classifiers. |