Rule Evaluation Measures: A Unifying View

Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and subgroup discovery, and support and confidence in association rule learning. Additional measures are needed as many descriptive knowledge discovery tasks involve the induction of a large set of redundant rules and the problem is the ranking and filtering of the induced rule set. In this paper we develop a unifying view on some of the existing measures for predictive and descriptive induction. We provide a common terminology and notation by means of contingency tables. We demonstrate how to trade off these measures, by using what we call weighted relative accuracy. The paper furthermore demonst...

• Type: Conference
• Year: 1999
• Where: ILP
• Authors: Nada Lavrac, Peter A. Flach, Blaz Zupan
• Keywords: Automated Reasoning, Descriptive Knowledge Discovery, ILP 1999, Knowledge Discovery, Predictive Knowledge Discovery