Abstract
The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their promising results on standard datasets. We propose a novel associative classifier for uncertain data. Our method, Uncertain Associative Classifier (UAC) is efficient and has an effective rule pruning strategy. Our experimental results on real datasets show that in most cases, UAC reaches better accuracies than the state of the art algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sen, P., Deshpande, A.: Representing and querying correlated tuples in probabilistic databases. In: IEEE ICDE, pp. 596–605 (2007)
Wang, C., Yuan, L.-Y., You, J.H., Zaiane, O.R., Pei, J.: On pruning for top-k ranking in uncertain databases. In: International Conference on Very Large Data Bases (VLDB), PVLDB, vol. 4(10) (2011)
Cheema, M.A., Lin, X., Wang, W., Zhang, W., Pei, J.: Probabilistic reverse nearest neighbor queries on uncertain data. IEEE Transactions on Knowledge and Data Engeneering (TKDE) 22, 550–564 (2010)
Aggarwal, C.C., Li, Y., Wang, J., Wang, J.: Frequent pattern mining with uncertain data. In: ACM SIGKDD, pp. 29–38 (2009)
Jiang, B., Pei, J.: Outlier detection on uncertain data: Objects, instances, and inference. In: IEEE ICDE (2011)
Antonie, M.-L., Zaiane, O.R., Holte, R.: Learning to use a learned model: A two-stage approach to classification. In: IEEE ICDM, pp. 33–42 (2006)
Bi, J., Zhang, T.: Support vector classification with input data uncertainty. In: Advances in Neural Information Processing Systems (NIPS), pp. 161–168 (2004)
Qin, B., Xia, Y., Li, F.: DTU: A Decision Tree for Uncertain Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 4–15. Springer, Heidelberg (2009)
Ge, J., Xia, Y., Nadungodage, C.: UNN: A Neural Network for Uncertain Data Classification. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6118, pp. 449–460. Springer, Heidelberg (2010)
Qin, B., Xia, Y., Li, F.: A bayesian classifier for uncertain data. In: ACM Symposium on Applied Computing, pp. 1010–1014 (2010)
Qin, B., Xia, Y., Prabhakar, S., Tu, Y.: A rule-based classification algorithm for uncertain data. In: IEEE ICDE (2009)
Gao, C., Wang, J.: Direct mining of discriminative patterns for classifying uncertain data. In: ACM SIGKDD, pp. 861–870 (2010)
Qin, X., Zhang, Y., Li, X., Wang, Y.: Associative Classifier for Uncertain Data. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds.) WAIM 2010. LNCS, vol. 6184, pp. 692–703. Springer, Heidelberg (2010)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: ACM SIGKDD, pp. 80–86 (1998)
Zaiane, O., Antonie, M.-L.: Classifying text documents by associating terms with text categories. In: Australasian Database Conference, pp. 215–222 (January 2002)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: IEEE ICDM, pp. 369–376 (2001)
Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: ACM SIGMOD, pp. 819–832 (2008)
Bernecker, T., Kriegel, H.P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic frequent itemset mining in uncertain databases. In: ACM SIGKDD (2009)
Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers (1993)
Demsar, J.: Statistical comparison of classifiers over multiple data sets. JMLR 7, 1–30 (2010)
Hooshsadat, M.: Classification and Sequential Pattern Mining From Uncertain Datasets. MSc dissertation, University of Alberta, Edmonton, Alberta (September 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hooshsadat, M., Zaïane, O.R. (2012). An Associative Classifier for Uncertain Datasets. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-30217-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
Online ISBN: 978-3-642-30217-6
eBook Packages: Computer ScienceComputer Science (R0)