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Rough Sets Based Inequality Rule Learner for Knowledge Discovery
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, Avdelningen för matematik och naturvetenskap.ORCID-id: 0000-0002-9920-7946
Ansvarlig organisasjon
2012 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Traditional rule learners employ equality relations between attributes and values to express decision rules. However, inequality relationships, as supplementary relations to equation, can make up a new function for complex knowledge acquisition. We firstly discuss an extended compensatory model of decision table, and examine how it can simultaneously express both equality and inequality relationships of attributes and values. In order to cope with large-scale compensatory decision table, we propose a scalable inequality rule leaner, which initially compresses the input spaces of attribute value pairs. Example and experimental results show that the proposed learner can generate compact rule sets that maintain higher classification accuracies than equality rule learners.

sted, utgiver, år, opplag, sider
Chengdu: Springer , 2012.
Emneord [en]
Classification, machine learning, rough sets, rule induction, inequality rule.
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-7256DOI: 10.1007/978-3-642-32115-3_11Lokal ID: oai:bth.se:forskinfo878E309FD22BE702C1257A6100590125ISBN: 978-3-642-32114-6 (tryckt)OAI: oai:DiVA.org:bth-7256DiVA, id: diva2:834846
Konferanse
8th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2012
Merknad

Lecture Notes in Computer Science, 2012, Volume 7413/2012, 100-105,

Tilgjengelig fra: 2012-09-18 Laget: 2012-08-21 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Liu, YangBai, GuohuaRakus-Andersson, Elisabeth

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Totalt: 73 treff
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