Generality is predictive of prediction accuracy

Webb, Geoffrey and Brain, Damien 2002, Generality is predictive of prediction accuracy, in Proceedings of PKAW '02 : the 2002 Pacific Rim Knowledge Aquisition Workshop, JSAI NII, Japan, pp. 117-130.

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Title Generality is predictive of prediction accuracy
Author(s) Webb, Geoffrey
Brain, Damien
Conference name Pacific Rim International Conference on Artificial Intelligence (7th : 2002 : Tokyo, Japan)
Conference location Tokyo, Japan
Conference dates 18-19 Aug. 2002
Title of proceedings Proceedings of PKAW '02 : the 2002 Pacific Rim Knowledge Aquisition Workshop
Editor(s) Yamaguchi, Takahira
Publication date 2002
Start page 117
End page 130
Publisher JSAI NII
Place of publication Japan
Summary During knowledge acquisition multiple alternative potential rules all appear equally credible. This paper addresses the dearth of formal analysis about how to select between such alternatives. It presents two hypotheses about the expected impact of selecting between classification rules of differing levels of generality in the absence of other evidence about their likely relative performance on unseen data. It is argued that the accuracy on unseen data of the more general rule will tend to be closer to that of a default rule for the class than will that of the more specific rule. It is also argued that in comparison to the more general rule, the accuracy of the more specific rule on unseen cases will tend to be closer to the accuracy obtained on training data. Experimental evidence is provided in support of these hypotheses. We argue that these hypotheses can be of use in selecting between rules in order to achieve specific knowledge acquisition objectives.
ISBN 4915905063
9784915905063
Language eng
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009562

Document type: Conference Paper
Collection: School of Information Technology
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