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On handling conflicts between rules with numerical features
KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
2006 (engelsk)Inngår i: Proc ACM Symp Appl Computing, 2006, s. 37-41Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Rule conflicts can arise in machine learning systems that utilise unordered rule sets. A rule conflict is when two or more rules cover the same example but differ in their majority classes. This conflict must be solved before a classification can be made. The standard methods for solving this type of problem are to use naive Bayes to solve the conflict or using the most frequent class (CN2). This paper studies the problem of rule conflicts in the area of numerical features. A novel family of methods, called distance based methods, for solving rule conflicts in continuous domains is presented. An empirical evaluation between a distance based method, CN2 and naive Bayes is made. It is shown that the distance based method significantly outperforms both naive Bayes and CN2.

sted, utgiver, år, opplag, sider
2006. s. 37-41
Serie
Proceedings of the ACM Symposium on Applied Computing ; 1
Emneord [en]
Numerical features, Rule conflicts, Rule learning, Classification (of information), Numerical methods, Problem solving, Set theory, Learning systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-155963Scopus ID: 2-s2.0-33751052308ISBN: 1595931082 (tryckt)ISBN: 9781595931085 (tryckt)OAI: oai:DiVA.org:kth-155963DiVA, id: diva2:766147
Konferanse
2006 ACM Symposium on Applied Computing, 23-27 April 2006, Dijon, France
Merknad

QC 20141126

Tilgjengelig fra: 2014-11-26 Laget: 2014-11-17 Sist oppdatert: 2014-11-26bibliografisk kontrollert

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Totalt: 33 treff
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