Boosting an associative classifier

Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent devel...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2006-07, Vol.18 (7), p.988-992
Main Authors: Sun, Y., Wang, Y., Wong, A.K.C.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
association mining
Associative
Boosting
Classification
classifier design and evaluation
Classifiers
Computational complexity
Computer science
control theory
systems
Data mining
Data processing. List processing. Character string processing
Exact sciences and technology
Explosives
Humans
Learning
Memory organisation. Data processing
Mining
pattern discovery
Software
Strategy
Sun
System testing
Test pattern generators
Voting
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Summary:Associative classification is a new classification approach integrating association mining and classification. It becomes a significant tool for knowledge discovery and data mining. However, high-order association mining is time consuming when the number of attributes becomes large. The recent development of the AdaBoost algorithm indicates that boosting simple rules could often achieve better classification results than the use of complex rules. In view of this, we apply the AdaBoost algorithm to an associative classification system for both learning time reduction and accuracy improvement. In addition to exploring many advantages of the boosted associative classification system, this paper also proposes a new weighting strategy for voting multiple classifiers.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.105