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 conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. In this paper, we propose a measure called prediction confidence to measure the prediction accuracy of association rules. In addition, a probabilistic-based approach for estimating prediction confidence of association rules is given and its performance is evaluated. The use of prediction confidence helps improve the performance of associative classifiers.
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© 2006 Springer-Verlag Berlin Heidelberg
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Do, T.D., Hui, S.C., Fong, A.C.M. (2006). Associative Classification with Prediction Confidence. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_21
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DOI: https://doi.org/10.1007/11739685_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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