Using multi decision tree technique to improving decision tree classifier

Authors:
Faiz Maazouzi;Halima Bahi
Affiliations:
LabGED Laboratory, The University of Badji Mokhtar-Annaba, BP. 12, Algeria;LabGED Laboratory, The University of Badji Mokhtar-Annaba, BP. 12, Algeria
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2012

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Abstract

The automatic classification systems, prediction and data mining are used in many applications marketing, finance, customer relationship management... using large databases. In this paper we describe a new data mining approach based on decision trees. In the proposed approach we built a multi-layer decision tree model, where each layer consists of several decision trees. The aim of the multi decision tree MDT is to improve decision tree classifier. The performances of MDT are compared with C4.5 decision tree algorithm and some ensemble of decision tree classifiers, namely bagging decision tree, boosting decision trees BDT and random forests decision tree. Results show substantial improvements when compared to these approaches.