Title: Using multi decision tree technique to improving decision tree classifier

Authors: Faiz Maazouzi; Halima Bahi

Addresses: LabGED Laboratory, The University of Badji Mokhtar-Annaba, BP. 12, Algeria ' LabGED Laboratory, The University of Badji Mokhtar-Annaba, BP. 12, Algeria

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.

Keywords: machine learning; decision trees; data mining; decision tree algorithms; classification; multi-layer decision trees; decision tree classifiers.

DOI: 10.1504/IJBIDM.2012.051712

International Journal of Business Intelligence and Data Mining, 2012 Vol.7 No.4, pp.274 - 287

Received: 20 Aug 2012
Accepted: 20 Sep 2012
Published online: 12 Nov 2014 *

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