Induction of decision trees in numeric domains using set-valued attributes

Article type: Research Article

Authors: Kalles, Dimitrios | Papagelis, Athanasios | Ntoutsi, Eirini

Affiliations: Computer Technology Institute, PO Box 1122, GR-261 10, Patras, Greece. E-mail: [email protected], [email protected], [email protected]

Abstract: Conventional algorithms for decision tree induction use an attribute-value representation scheme for instances. This paper explores the empirical consequences of using set-valued attributes. This simple representational extension, when used as a pre-processor for numeric data, is shown to yield significant gains in accuracy combined with attractive build times. It is also shown to improve the accuracy for the second best classification option, which has valuable ramifications for post-processing. To do so an intuitive and practical version of pre-pruning is employed. Moreover, the implementation of a simple pruning scheme serves as an example of pruning applicability over the resulted trees and also as an indication that the proposed discretization absorbs much of pruning potential. Finally, we construct several versions of the basic algorithm to examine the value of every component that comprises it.

Keywords: decision trees, discretization, set-values, post-processing

DOI: 10.3233/IDA-2000-43-411

Journal: Intelligent Data Analysis, vol. 4, no. 3-4, pp. 323-347, 2000