Rotation-based model trees for classification. (4th December 2009)

• Record Type: Journal Article
• Authors: Kotsiantis, S.B.
• Is Part Of: International journal of data analysis techniques and strategies. Volume 2:Number 1(2010)
• Page Start: 22
• Page End: 37
• Publication Date: 2009-12-04
• Subjects: machine learning -- classifier ensembles -- combining models -- model trees -- classification -- decision trees -- rotation-based ensemble; Electronic data processing -- Periodicals; Database searching -- Periodicals; 005
• Languages: English
• ISSNs: 1755-8050

Abstract

Structurally, a model tree is a regression method that takes the form of a decision tree with linear regression functions instead of terminal class values at its leaves. In this study, model trees were coupled with a rotation-based ensemble for solving classification problems. In order to apply this regression technique to classification problems, we considered the conditional class probability function and sought a model-tree approximation to it. During classification, the class whose model tree generated the greatest approximated probability value was chosen as the predicted class. We performed a comparison with other well-known ensembles of decision trees on standard benchmark data sets, and the performance of the proposed technique was greater in most cases.