Abstract
The two main challenges associated with mining data streams are concept drifting and data noise. Current algorithms mainly depend on the robust of the base classifier or learning ensembles, and have no active mechanisms to deal noisy. However, noise still can induce the drastic drops in accuracy. In this paper, we present a clustering-based method to filter out hard instances and noise instances from data streams. We also propose a trigger to detect concept drifting and build RobustBoosting, an ensemble classifier, by boosting the hard instances. We evaluated RobustBoosting algorithm and AdaptiveBoosting algorithm [1] on the synthetic and real-life data sets. The experiment results show that the proposed method has substantial advantage over AdaptiveBoosting algorithm in prediction accuracy, and that it can converge to target concepts efficiently with high accuracy on datasets with noise level as high as 40%.
This research is supported by NSF 60373108.
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References
Chu, F., Zaniolo, C.: Fast and Light Boosting for Adaptive Mining of Data Streams. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, Springer, Heidelberg (2004)
Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proc. of the Sixth International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Hulten, G., Spencer, L., Domingos, P.: Mining Time-changing Data Streams. In: ACM SIGKDD (2001)
Last, M.: Online Classification of Nonstationary Data Stream. Intelligent Data Analysis 6, 129–147 (2002)
Kuncheva, L.I.: Classifier Ensembles for Changing Environments. In: Proc. 5th Int. Workshop on Multiple Classifier Systems, pp. 1–15 (2004)
Street, W., Kim, Y.: A Streaming Ensemble Algorithm(sea) for Large–Scale Classification. In: Int’l. Conf. on Knowledge Discovery and Data Mining (2001)
Wang, H., Wei Fan, P., Yu, J.: Han: Mining Concept-Drifting Data Streams Using Ensemble Classifiers. In: Int’l. Conf. on Knowledge Discovery and Data Mining (2003)
Kubica, J., Moore, A.: Probabilistic Noise Identification and Data Cleaning. In: Int’l. Conf. Data Mining (2003)
Zhu, X., Wu, X., Chen, Q.: Eliminating Class Noise in Large Datasets. In: The Proc. of the 20th International Conf. on Maching Learning (2003)
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine learning 23, 69–101 (1996)
Fan, W.: Systematic Data Selection to Mine Concept-Drifting Data Streams. In: The Proceeding of the Conf. KDD, pp. 128–137 (2004)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Database with Noise. In: Proc. of Int. Conf. on Knowledge Discovering and Data Mining, pp. 226–231 (1996)
Kalai, A., Rocco, A.: Servedio: Boosting in the Presence of Noise. Journal of Computer and System Science 71(3), 226–290 (2003)
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Wang, Y., Li, Z., Zhang, Y. (2006). Classifying Noisy Data Streams. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_65
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DOI: https://doi.org/10.1007/11881599_65
Publisher Name: Springer, Berlin, Heidelberg
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