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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-642-22000-5_45
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