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Feature Based Rule Learner in Noisy Environment Using Neighbourhood Rough Set Model
Responsible organisation
2010 (English)In: International Journal of Software Science and Computational Intelligence, ISSN 1942-9045, E-ISSN 1942-9037, Vol. 2, no 2, 66-85 p.Article in journal (Refereed) Published
Abstract [en]

From the perspective of cognitive informatics, cognition can be viewed as the acquisition of knowledge. In real-world applications, information systems usually contain some degree of noisy data. A new model proposed to deal with the hybrid-feature selection problem combines the neighbourhood approximation and variable precision rough set models. Then rule induction algorithm can learn from selected features in order to reduce the complexity of rule sets. Through proposed integration, the knowledge acquisition process becomes insensitive to the dimensionality of data with a pre-defined tolerance degree of noise and uncertainty for misclassification. When the authors apply the method to a Chinese diabetic diagnosis problem, the hybrid-attribute reduction method selected only five attributes from totally thirty-four measurements. Rule learner produced eight rules with average two attributes in the left part of an IF-THEN rule form, which is a manageable set of rules. The demonstrated experiment shows that the present approach is effective in handling real-world problems.

Place, publisher, year, edition, pages
IGI Publishing , 2010. Vol. 2, no 2, 66-85 p.
Keyword [en]
Cognitive informatics, knowledge discovery, neighbourhood approximation, rough set, attributes reduction, noisy data, LERS data mining system, rule induction, classification.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-7582DOI: 10.4018/jssci.2010040104Local ID: oai:bth.se:forskinfoFBBBD3688F5C9A6FC125782200119489OAI: oai:DiVA.org:bth-7582DiVA: diva2:835224
Available from: 2012-09-18 Created: 2011-01-24 Last updated: 2015-06-30Bibliographically approved

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Citation style
  • apa
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Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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