Abstract
In this paper, we propose to combine the naive-Bayes approach with CVFDT, which is known as one of the major algorithms to induce a high-accuracy decision tree from time-changing data streams. The proposed improvement, called CVFDTNBC, induces a decision tree as CVFDT does, but contains naive-Bayes classifiers in the leaf nodes of the induced decision tree. The experiment using the artificially generated time-changing data streams shows that CVFDTNBC can induce a decision tree with more accuracy than CVFDT does.
Keywords
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
Domingos, P., Pazzani, M.: On the Optimality of the Simple Bayesian Classifiers under Zero-One Loss. Machine Learning 29, 103–130 (1997)
Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discretization of Continuous Features. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 194–202 (1995)
Gama, J., Rocha, R., Medas, P.: Accurate Decision Trees for Mining High-speed Data Streams. In: Proceedings of the Nineth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528 (2003)
Gama, J., Medas, P., Rodrigues, P.: Learning Decision Trees from Dynamic Data Streams. In: Proceedings of the 2005 ACM Symposium on Applied computing, pp. 573–577 (2005)
Han, J., Kamber, M.: Data Mining: Concepts and Techiniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Hulten, G., Spencer, L., Domingos, P.: Mining Time-changing Data Stream. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pp. 97–106 (2001)
Hulten, G., Domingos, P.: VFML – A Toolkit for Mining High-speed Time-changing Data Streams (2003), http://www.cs.washington.edu/dm/vfml/
Kubat, M., Widmer, G.: Adapting to Drift in Continuous Domains. In: Proceedings of the Eighth European Conference on Machine Learning, pp. 307–310 (1995)
Klinkenberg, R., Joachims, T.: Detecting Concept Drift with Support Vector Machines. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 487–494 (2000)
Kohavi, R.: Scaling Up the Accuracy of Naive- Bayes Classifiers: a Decision-Tree Hybrid. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Kohavi, R., Sahami, M.: Error-Based and Entropy-Based Discretization of Continuous Features. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 114–119 (1996)
Langley, P., Iba, W., Thompson, K.: An Analysis of Bayesian Classifiers. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 223–228 (1992)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Quinlan, J.R.: Improved Use of Continuous Attributes in C4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Widmer, G., Kubat, M.: Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning 23, 69–101 (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nishimura, S., Terabe, M., Hashimoto, K., Mihara, K. (2008). Learning Higher Accuracy Decision Trees from Concept Drifting Data Streams. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-69052-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69045-0
Online ISBN: 978-3-540-69052-8
eBook Packages: Computer ScienceComputer Science (R0)