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Using Genetic Programming to Increase Rule Quality
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
2008 (Engelska)Ingår i: In Proceedings of the Twenty-First International FLAIRS Conference, AAAI Press , 2008, s. 288-293Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Rule extraction is a technique aimed at transforming highly accurate opaque models like neural networks into comprehensible models without losing accuracy. G-REX is a rule extraction technique based on Genetic Programming that previously has performed well in several studies. This study has two objectives, to evaluate two new fitness functions for G-REX and to show how G-REX can be used as a rule inducer. The fitness functions are designed to optimize two alternative quality measures, area under ROC curves and a new comprehensibility measure called brevity. Rules with good brevity classifies typical instances with few and simple tests and use complex conditions only for atypical examples. Experiments using thirteen publicly available data sets show that the two novel fitness functions succeeded in increasing brevity and area under the ROC curve without sacrificing accuracy. When compared to a standard decision tree algorithm, G-REX achieved slightly higher accuracy, but also added additional quality to the rules by increasing their AUC or brevity significantly.

Ort, förlag, år, upplaga, sidor
AAAI Press , 2008. s. 288-293
Nyckelord [en]
genetic programming, rule extraction, roc curve, optimzation, Computer Science, Artificial Intelligence, Data Mining
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Identifikatorer
URN: urn:nbn:se:hb:diva-5926Lokalt ID: 2320/3598ISBN: 978-1-57735-365-2 (tryckt)OAI: oai:DiVA.org:hb-5926DiVA, id: diva2:886609
Konferens
International FLAIRS Conference
Tillgänglig från: 2015-12-22 Skapad: 2015-12-22 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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König, RikardJohansson, Ulf

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Totalt: 113 träffar
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