The full text of this article


Constructing decision trees with multiple response variables
by Seong-Jun Kim, Kang Bae Lee
International Journal of Management and Decision Making (IJMDM), Vol. 4, No. 4, 2003


Abstract: Data mining is a process of discovering meaningful patterns in large data sets that are useful for decision making and has recently received an amount of attention in a wide range of business and engineering fields. Decision tree, also known as recursive partitioning or rule induction, is one of the most frequently used methods for data mining. A decision tree, on a divide-and-conquer basis, provides a set of rules for classifying samples in the learning data set. Most of works on decision tree have been conducted for the case of single response variable. However, situations where multiple response variables should be considered arise from many applications, for example, manufacturing process monitoring, customer management, and clinical and health analysis. This article concerns constructing decision trees when there are two or more response variables in the data set. In this article, we investigate node homogeneity criteria such as entropy and Gini index and then present three approaches to constructing decision trees with multiple response variables. To do so, we first describe extensions of entropy and a Gini index to the case in which multiple response variables are of concern. A weighting method for node splitting is also explained. Next, we present a decision tree minimising an expected loss due to misclassifications. To illustrate the procedures, numerical examples are given with discussions.


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 Management and Decision Making (IJMDM):
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