Abstract
Meta-learning system for KDD is an open and evolving platform for efficient testing and intelligent recommendation of data mining process. Meta-learning is adopted to automate the selection and arrangement of algorithms in the mining process of a given application. Execution engine is the kernel of the system to provide mining strategies and services. An extensible architecture is presented for this engine based on mature multi-agent environment, which connects different computing hosts to support intensive computing and complex process control distributedly. Reuse of existing KDD algorithms is achieved by encapsulating them into agents. We also define a data mining workflow as the input of our engine and detail the coordination process of various agents to process it. To take full advantage of the distributed computing resources, an execution tree and a load balance model are designed too.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Vilalta, R., Giraud-Carrier, C., Brazdil, P., Soares, C.: Using Meta-Learning to Support Data Mining. International Journal of Computer Science Applications I(1), 31–45 (2004)
Vilalta, R., Drissi, Y.A.: Perspective View and Survey of Meta-Learning. Journal of Artificial Intelligence Review 18(2), 77–95 (2002)
Nakhaeizadeh, G., Schnabel, A.: Development of Multi-criteria Metrics for Evaluation of Data-mining Algorithms. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Ming (1997)
Soares, C., Brazdil, P.: Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information. In: Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases (2000)
Jennings, N.R., Wooldridge, M.J.: Applications Of Intelligent Agents. In: Agent Technology Foundations, Applications and Markets. Springer, Heidelberg (1998)
Shi, Z., Zhang, H., Cheng, Y., Jiang, Y., Sheng, Q., Zhao, Z.: MAGE: An Agent-Oriented Programming Environment. In: Proc. IEEE-ICCI, pp. 250–257 (2004)
Cannataro, M., Talia, D., Trunfio, P.: Distributed data mining on the grid. Future Generation Computer Systems 18(8), 1101–1112 (2002)
BotÃa, J.A., Gómez-Skarmeta, A.F., Velasco, J.R., Garijo, M.: A Proposal for Meta-Learning Through a Multi-Agent System. In: Agents Workshop on Infrastructure for Multi-Agent Systems, pp. 226–233 (2000)
BotÃa, J.A., Gómez-Skarmeta, A.F., Valdés, M., Padilla, A.: METALA: A Meta-learning Architecture. Fuzzy Days, 688–698 (2001)
Chan, P., Stolfo, S.: On the Accuracy of Meta-Learning for Scalable Data Mining. Journal of Intelligent Integration of Information, Ed. L. Kerschberg (1998)
Friedman, J., Hastie, T., Tibshirani, R.: Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics 28, 337–387 (2000)
Sohn, S.Y.: Meta Analysis of Classification Algorithms for Pattern Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(11), 1137–1144 (1999)
Leite, R., Brazdil, P.: Improving Progressive Sampling via Meta-learning on Learning Curves. In: Proc. ECML (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, P., He, Q., Huang, R., Lin, F., Shi, Z. (2005). Execution Engine of Meta-learning System for KDD in Multi-agent Environment. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds) Autonomous Intelligent Systems: Agents and Data Mining. AIS-ADM 2005. Lecture Notes in Computer Science(), vol 3505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492870_12
Download citation
DOI: https://doi.org/10.1007/11492870_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26164-3
Online ISBN: 978-3-540-31932-0
eBook Packages: Computer ScienceComputer Science (R0)