Abstract
The main contribution of the paper is proposing and evaluating, through the computational experiment, an agent-based population learning algorithm generating a representative training dataset of the required size. The proposed approach is based on the assumption that prototypes are selected from clusters. Thus, the number of clusters produced has a direct influence on the size of the reduced dataset. Agents within an A-Team execute various local search procedures and cooperate to find-out a solution to the instance reduction problem aiming at obtaining a compact representation of the dataset. Computational experiment has confirmed that the proposed algorithm is competitive to other approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. School of Information and Computer Science. University of California, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Barbucha, D., Czarnowski, I., Jędrzejowicz, P., Ratajczak-Ropel, E., Wierzbowska, I.: e-JABAT - An Implementation of the Web-Based A-Team. In: Nguyen, N.T., Jain, I.C. (eds.) Intelligent Agents in the Evolution of Web and Applications. SCI, vol. 167, pp. 57–86. Springer, Heidelberg (2009)
Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper. Exp. 3(3), 6–20 (2003)
Czarnowski, I., Jędrzejowicz, P.: An Approach to Data Reduction and Integrated Machine Classification. New Generation Computing 28(1), 21–40 (2010)
Czarnowski, I., Jędrzejowicz, P.: An Approach to Instance Reduction in Supervised Learning. In: Coenen, F., Preece, A., Macintosh, A. (eds.) Research and Development in Intelligent Systems XX, pp. 267–282. Springer, London (2004)
Czarnowski, I., Jędrzejowicz, P.: Cluster Integration for the Cluster-Based Instance Selection. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 353–362. Springer, Heidelberg (2010)
Datasets used for classification: comparison of results. directory of data sets, http://www.is.umk.pl/projects/datasets.html (accessed September 1, 2009)
Hamo, Y., Markovitch, S.: The COMPSET Algorithm for Subset Selection. In: Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, Edinburgh, Scotland, pp. 728–733 (2005)
Jędrzejowicz, J., Jędrzejowicz, P.: Cellular GEP-Induced Classifiers. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 343–352. Springer, Heidelberg (2010)
Jędrzejowicz, P.: Social Learning Algorithm as a Tool for Solving Some Difficult Scheduling Problems. Foundation of Computing and Decision Sciences 24, 51–66 (1999)
Kim, S.-W., Oommen, B.J.: A Brief Taxonomy and Ranking of Creative Prototype Reduction Schemes. Pattern Analysis Application 6, 232–244 (2003)
Klusch, M., Lodi, S., Moro, G.: Agent-Based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, pp. 104–122. Springer, Heidelberg (2003)
Krishnaswamy, S., Zaslavsky, A., Loke, S.W.: Techniques for Estimating the Computation and Communication Costs of Distributed Data Mining. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J., Hoekstra, A.G. (eds.) ICCS-ComputSci 2002. LNCS, vol. 2329, pp. 603–612. Springer, Heidelberg (2002)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, SanMateo (1993)
Silva, J., Giannella, C., Bhargava, R., Kargupta, H., Klusch, M.: Distributed Data Mining and Agents. Engineering Applications of Artificial Intelligence Journal 18, 791–807 (2005)
Talukdar, S., Baerentzen, L., Gove, A., de Souza, P.: Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents. Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)
Uno, T.: Multi-sorting Algorithm for Finding Pairs of Similar Short Substrings from Large-scale String Data. Knowledge and Information Systems (2009); doi: 10.1007/s10115-009-0271-6
Vucetic, S., Obradovic, Z.: Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 29-39 (2000)
Wilson, D.R., Martinez, T.R.: Reduction Techniques for Instance-based Learning Algorithm. Machine Learning 33(3), 257–286 (2000)
Yu, K., Xiaowei, X., Ester, M., Kriegel, H.-P.: Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach. Knowledge and Information Systems 5(2), 201–224 (2004)
Zhu, X., Wu, X.: Scalable Representative Instance Selection and Ranking. In: IEEE Proceedings of the 18th International Conference on Pattern Recognition, vol. 3, pp. 352–355 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Czarnowski, I., Jędrzejowicz, P. (2011). A New Cluster-based Instance Selection Algorithm. In: O’Shea, J., Nguyen, N.T., Crockett, K., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2011. Lecture Notes in Computer Science(), vol 6682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22000-5_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-22000-5_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21999-3
Online ISBN: 978-3-642-22000-5
eBook Packages: Computer ScienceComputer Science (R0)