Using multi decision tree technique to improving decision tree classifier. (28th January 2013)

• Record Type: Journal Article
• Authors: Maazouzi, Faiz; Bahi, Halima
• Is Part Of: International journal of business intelligence and data mining. Volume 7:Number 4(2012)
• Page Start: 274
• Page End: 287
• Publication Date: 2013-01-28
• Subjects: machine learning -- decision tree -- data mining -- decision tree algorithm -- data mining techniques -- classification; 006.312
• Languages: English
• ISSNs: 1743-8187

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.