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

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 is shown to yield significant gains in speed and accuracy. To do so, the paper also describes an intuitive and practical version of pre-pruning. This method is shown to yield considerably better accuracy results when used as a pre-processor for numeric data. It is also shown to improve the accuracy for the second best classification option, which has valuable ramifications for post-processing.

• Type: Journal
• Year: 2000
• Where: IDA
• Authors: Dimitrios Kalles, Athanassios Papagelis, Eirini Ntoutsi
• Keywords: Attribute-value Representation Scheme, Decision Tree Induction, IDA 2000, Information Technology, Simple Representational Extension