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Rough Sets Based Inequality Rule Learner for Knowledge Discovery
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-9920-7946
Responsible organisation
2012 (English)Conference paper, (Refereed)
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.

Place, publisher, year, edition, pages
Chengdu: Springer , 2012.
Keyword [en]
Classification, machine learning, rough sets, rule induction, inequality rule.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-7256DOI: 10.1007/978-3-642-32115-3_11Local ID: oai:bth.se:forskinfo878E309FD22BE702C1257A6100590125ISBN: 978-3-642-32114-6 (print)OAI: oai:DiVA.org:bth-7256DiVA: diva2:834846
Conference
8th International Conference on Rough Sets and Current Trends in Computing, RSCTC 2012
Note

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

Available from: 2012-09-18 Created: 2012-08-21 Last updated: 2016-09-20Bibliographically approved

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Liu, YangBai, GuohuaRakus-Andersson, Elisabeth
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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