Prediction Confidence for Associative Classification

Do, T. D., Hui, S. C. and Fong, A. C.M. (2005) Prediction Confidence for Associative Classification. In: International Conference on Machine Learning and Cybernetics 2005, Guangzhou, China, 8-21 Aug 2005, 1993-1998 Vol. 4. (doi: 10.1109/ICMLC.2005.1527272)

Abstract

Associative classification, which uses association rules for classification, has achieved high accuracy in comparison with other classification approaches. However, the confidence measure, which is used to select association rules for classification, may not conform to the prediction accuracy of the rules. In this paper, we propose a measure for association rules called prediction confidence to measure the accuracy of the prediction of association rules. An approach for estimating the prediction confidence of a rule is also given. The use of prediction confidence instead of confidence measure helps gather better association rules for associative classification. As a result, a more accurate associative classifier can be constructed using the prediction confidence measure.

• Item Type: Conference Proceedings
• Additional Information: Print ISBN: 0780390911
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Do, T. D., Hui, S. C., and Fong, A. C.M.
• College/School: College of Science and Engineering > School of Computing Science