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Integrating classification capability and reliability in associative classification: a β-stronger model
journal contribution
posted on 2010-05-01, 00:00 authored by Y Jiang, Y Liu, Xiao LiuXiao Liu, S YangMining class association rules is an important task for associative classification and plays a key role in rule-based decision support systems. Most of the existing methods try the best to mine rules with high reliability but ignore their capability for classifying potential objects. This paper defines a concept of β-stronger relationship, and proposes a new method that integrates classification capability and classification reliability in rule discovery. The method takes advantage of rough classification method to generate frequent items and rules, and calculate their support and confidence degrees. We propose two new theorems to prune redundant frequent items and a concept of indiscernibility relationship between rules to prune redundant rules. The pruning theorems afford the associative classifier with good classification capability. The experiment shows that the proposed method generates a smaller frequent item set and significantly enhances the classification performance.
History
Journal
Expert systems with applicationsVolume
37Issue
5Pagination
3953 - 3961Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0957-4174Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2009, Elsevier Ltd.Usage metrics
Categories
No categories selectedKeywords
Associative classificationClassification capabilityClassification reliabilityβ-Stronger relationshipPruning theoremScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringbeta-Stronger relationship