Article

Rule Evaluation Measures: A Unifying View

Authors:

Nada Lavrac,

Peter A. Flach,

Blaz ZupanAuthors Info & Claims

ILP '99: Proceedings of the 9th International Workshop on Inductive Logic Programming

Pages 174 - 185

Published: 24 June 1999 Publication History

Abstract

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 demonstrates that many rule evaluation measures developed for predictive knowledge discovery can be adapted to descriptive knowledge discovery tasks.

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Published In

ILP '99: Proceedings of the 9th International Workshop on Inductive Logic Programming

June 1999

297 pages

ISBN:3540661093

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 June 1999

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Wang TDesai BDesai BAnagnostopoulos DManolopoulos YNikolaidou M(2019)On the appropriate pattern frequentness measure and pattern generation modeProceedings of the 23rd International Database Applications & Engineering Symposium10.1145/3331076.3331125(1-15)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1145/3331076.3331125
Mo?Ina MDemšar JBratko I?Abkar J(2019)Extreme value correctionMachine Language10.1007/s10994-018-5731-3108:2(297-329)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10994-018-5731-3
Loyola-González OMartínez-Trinidad JCarrasco-Ochoa JGarcía-Borroto M(2016)Effect of class imbalance on quality measures for contrast patternsInformation Sciences: an International Journal10.1016/j.ins.2016.09.040374:C(179-192)Online publication date: 20-Dec-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.09.040
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