Abstract
An advanced approach to Bayesian classification is based on exploited patterns. However, traditional pattern-based Bayesian classifiers cannot adapt to the evolving data stream environment. For that, an effective Pattern-based Bayesian classifier for Data Stream (PBDS) is proposed. First, a data-driven lazy learning strategy is employed to discover local frequent patterns for each test record. Furthermore, we propose a summary data structure for compact representation of data, and to find patterns more efficiently for each class. Greedy search and minimum description length combined with Bayesian network are applied to evaluating extracted patterns. Experimental studies on real-world and synthetic data streams show that PBDS outperforms most state-of-the-art data stream classifiers.
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Notes
- 1.
\(P(y_i|\mathbf x ) = \frac{P(\mathbf x ,y_i)}{P(\mathbf x )}=\frac{P(y_i) \cdot P(\mathbf x |y_i)}{P(\mathbf x )}\).
- 2.
Datasets Chess, Connect-4, EEG, MAGIC, PokerHand and CoverType are downloaded from UCI Machine Learning Repository http://archive.ics.uci.edu/ml/; Others are generated by the classical data generators separately with 1,000,000 records via MOA.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (Nos. 61672086 and 61702030) and the Fundamental Research Funds for the Central Universities (Nos. 2016RC048 and 2016YJS036).
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Yuan, J., Wang, Z., Sun, Y., Zhang, W., Jiang, J. (2017). A Pattern-Based Bayesian Classifier for Data Stream. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_92
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