Using multi decision tree technique to improving decision tree classifier
by Faiz Maazouzi; Halima Bahi
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 7, No. 4, 2012

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.

Online publication date: Sun, 27-Jan-2013

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email