Evaluation of decision trees: a multi-criteria approach

Data mining (DM) techniques are being increasingly used in many modern organizations to retrieve valuable knowledge structures from organizational databases, including data warehouses. An important knowledge structure that can result from data mining activities is the decision tree (DT) that is used for the classification of future events. The induction of the decision tree is done using a supervised knowledge discovery process in which prior knowledge regarding classes in the database is used to guide the discovery. The generation of a DT is a relatively easy task but in order to select the most appropriate DT it is necessary for the DM project team to generate and analyze a significant number of DTs based on multiple performance measures. We propose a multi-criteria decision analysis based process that would empower DM project teams to do thorough experimentation and analysis without being overwhelmed by the task of analyzing a significant number of DTs, which would offer a positive contribution to the DM process. We also offer some new approaches for measuring some of the performance criteria.