A Proposal for Using Continuous Attributes in Classification Trees

Classification trees have been successfully used in several application fields. However, continuous attributes cannot be used directly when building classification trees, but they must be first discretized with clustering techniques, which require some degree of subjectivity. We propose an approach to build classification trees that does not require the discretization of the continuous attributes. The approach is an extension of existing methods for building classification trees and is based on the information gain yielded by discrete and continuous attributes. Data from a software development case study are analyzed with both the proposed approach and C4.5 to show the approach's applicability and benefits over C4.5.