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Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers.
- Source :
- Intelligent Data Analysis; 2014 Supplement, Vol. 18, pS89-S100, 12p
- Publication Year :
- 2014
-
Abstract
- In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel "Dynamic K" satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the "Dynamic K" satisfaction mechanism allow improving the performance of CAR-based classifiers. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1088467X
- Volume :
- 18
- Database :
- Complementary Index
- Journal :
- Intelligent Data Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 100419714
- Full Text :
- https://doi.org/10.3233/IDA-140711