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Boosting an Associative Classifier
July 2006 (vol. 18 no. 7)
pp. 988-992
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.

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Index Terms:
Data mining, classification, association mining, classifier design and evaluation, pattern discovery, boosting.
Citation:
Yanmin Sun, Yang Wang, Andrew K.C. Wong, "Boosting an Associative Classifier," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 7, pp. 988-992, July 2006, doi:10.1109/TKDE.2006.105
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