A new method of mining data streams using harmony search
- 283 Downloads
Incremental learning has been used extensively for data stream classification. Most attention on the data stream classification paid on non-evolutionary methods. In this paper, we introduce new incremental learning algorithms based on harmony search. We first propose a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, we improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise. This improved version is called improved incremental harmony-based classifier. The proposed methods are evaluated on some real world and synthetic data sets. Experimental results show that the proposed batch classifier outperforms some batch classifiers and also the proposed incremental methods can effectively address the issues usually encountered in the data stream environments. Improved incremental harmony-based classifier has significantly better speed and accuracy on capturing concept drifts than the non-incremental harmony based method and its accuracy is comparable to non-evolutionary algorithms. The experimental results also show the robustness of improved incremental harmony-based classifier.
KeywordsData stream Classification Concept drift Harmony search
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions which improved the paper.
- Bifet, A., & Gavaldà, R. (2009a). Adaptive parameter-free learning from evolving data streams. In IDA.Google Scholar
- Bifet A., & Gavaldà, R. (2009b). Adaptive XML tree classification on evolving data streams. In Proc. of European conference on machine learning and knowledge discovery in databases, ECML/PKDD.Google Scholar
- Cunningham, P., Nowlan, N., Delany, S. J., & Haahr, M. (2003). A case-based approach to spam filtering that can track concept drift. Technical Report TCD-CS-2003-16, Ireland, Trinity College Dublin.Google Scholar
- EGEE: Enabling Grids for E-science in Europe. http://www.euegee.org. Accessed October 2011.
- Fan, W. (2004a). StreamMiner: A classifier ensemble-based engine to mine concept-drifting data streams. In Proc. of 2004 international conference on Very Large Data Bases (VLDB’2004) (Vol. 30, pp. 1257–1260). Toronto, Canada.Google Scholar
- Fan, W. (2004b). Systematic data selection to mine concept-drifting data stream. In Proc. of ACM SIGKDD (pp. 128–137). Seattle, Washington USA.Google Scholar
- Fogel, L. (1994). Evolutionary programming in perspective: The top-down view. In: J. M. Zurada, R. Marks II, C. Robinson (Eds.), Computational intelligence: Imitating life (pp. 135–146). Piscataway: IEEE Press.Google Scholar
- Gama, J., Medas, P., & Rocha, R. (2004). Forest trees for on-line data. In Proc. ACM symp. applied computing (SAC’04) (pp. 632–636).Google Scholar
- Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.Google Scholar
- Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd Edn.). Morgan Kaufmann Publisher.Google Scholar
- Hettich, S., & Bay, S. D. (2010). The UCI KDD Archive. University of California, Department of Information and Computer Science, Irvine, CA. http://www.kdd.ics.uci.edu.
- Holland, J. H. (1986). Escaping brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. In Machine learning: An artificial intelligence approach (Vol. II, pp. 593–623). Morgan Kaufmann.Google Scholar
- Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In F. Provost (Ed.), Knowledge discovery and data mining (pp. 97–106). AAAI Press.Google Scholar
- Klinkenberg, R. (2004). Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, 8(3), 281–300.Google Scholar
- Koza, J., & Poli, R. (2005). Genetic programming. In E. Burke & G. Kendall (Eds.), Introductory tutorials in optimization, decision support and search methodology (Chapter 5, pp. 127–164). Kluwer Press.Google Scholar
- Mukhopadhyay, A., Roy, A., Das, S., & Abraham, A. (2008). Population-variance and explorative power of harmony search: an analysis. In Proceedings of 3rd IEEE international conference on digital information management (ICDIM 2008) (pp. 13–16). London, United Kingdom.Google Scholar
- Street, W., & Kim, Y. (2001). A streaming ensemble algorithm for large scale classification. In Proceeding of the seventh international conference on knowledge discovery and data mining (pp. 377–382). NY.Google Scholar
- Wang, H., Fan, W., Yu, P., & Han, J. (2003). Mining concept-drifting data streams using ensemble classifiers. In Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD2003) (pp. 226–235). Washington, D.C.Google Scholar
- Widmer G., & Kubat, M. (1996). Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1), 69–101.Google Scholar
- Witten, I. H., & Frank, E. (1999). Data mining: Practical machine learning tools with Java implementations. San Francisco: Morgan Kaufmann.Google Scholar