A new approach for measuring rule set consistency
A new approach for measuring rule set consistency
Various algorithms are capable of learning a set of classification rules from a number of observations with their corresponding class labels. Whereas the obtained rule set is usually evaluated by measuring its accuracy on a number of unseen examples, there are several other evaluation criteria, such as comprehensibility and consistency, that are often overlooked. In this paper we focus on the aspect of consistency: if a rule learner is applied several times on the same data set, will it provide rule sets that are similar over the different runs? A new measure is proposed and various examples show how this new measure can be used to decide between different algorithms and rule sets or to find out whether the rules in a knowledge base need to be updated.
data mining, machine learning, rule extraction, consistency, model selection
167-182
Huysmans, J.
4926c4a3-4dd3-477f-a352-de2a432f2d61
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
October 2007
Huysmans, J.
4926c4a3-4dd3-477f-a352-de2a432f2d61
Baesens, B.
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Vanthienen, J.
808131f1-b77b-4dee-bdda-90a94124c999
Huysmans, J., Baesens, B. and Vanthienen, J.
(2007)
A new approach for measuring rule set consistency.
Data and Knowledge Engineering, 63 (1), .
(doi:10.1016/j.datak.2007.01.001).
Abstract
Various algorithms are capable of learning a set of classification rules from a number of observations with their corresponding class labels. Whereas the obtained rule set is usually evaluated by measuring its accuracy on a number of unseen examples, there are several other evaluation criteria, such as comprehensibility and consistency, that are often overlooked. In this paper we focus on the aspect of consistency: if a rule learner is applied several times on the same data set, will it provide rule sets that are similar over the different runs? A new measure is proposed and various examples show how this new measure can be used to decide between different algorithms and rule sets or to find out whether the rules in a knowledge base need to be updated.
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Published date: October 2007
Keywords:
data mining, machine learning, rule extraction, consistency, model selection
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Local EPrints ID: 51704
URI: http://eprints.soton.ac.uk/id/eprint/51704
ISSN: 0169-023X
PURE UUID: d8a9a403-4dd9-4837-a205-862788284dd9
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Date deposited: 20 Aug 2008
Last modified: 16 Mar 2024 03:39
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Author:
J. Huysmans
Author:
J. Vanthienen
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