Abstract
Distributed data mining is an important research area. The task of the distributed data mining is to analyze data from different sources. Solving such tasks requires a special approach and tools, different from those dedicated to learning from data located in a single database. This paper presents an approach to learning classifiers from distributed data based on data reduction (the prototype selection) at a local level. The problem is solved through applying the A-Team concept implemented using the JABAT environment, which supports implementation of multiple-agent teams. The paper includes a general overview of the JABAT, the problem formulation and some technical details of the proposed implementation. Finally, the computational experiment results validating the approach are shown.
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References
Aydin, M.E., Fogarty, T.C.: Teams of Autonomous Agents for Job-shop Scheduling Problems: An Experimental Study. Journal of Intelligent Manufacturing 15(4), 455–462 (2004)
Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper, Exp 3(3), 6–20 (2003)
Marinescu, D.C., Boloni, L.: A Component-based Architecture for Problem Solving Environments. Mathematics and Computers in Simulation 54, 279–293 (2000)
Parunak, H.V.D.: Agents in Overalls: Experiences and Issues in the Development and Deployment of Industrial Agent-Based Systems. International Journal of Cooperative Information Systems 9(3), 209–228 (2000)
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)
Jędrzejowicz, P., Wierzbowska, I.: JADE-Based A-Team Environment. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 719–726. Springer, Heidelberg (2006)
Kargupta, H., Park, B.-H., Hershberger, D., Johnson, E.: Collective Data Mining: A New Perspective Toward Distributed Data Analysis. In: Kargupta, H., Chan, P. (eds.) Accepted in the Advances in Distributed Data Mining, AAAI/MIT Press (1999)
Zhang, X.-F., Lam, C.-M., Cheung, W.K.: Mining Local Data Sources For Learning Global Cluster Model Via Local Model Exchange. IEEE Intelligence Informatics Bulletine 4(2) (2004)
Prodromidis, A., Chan, P.K., Stolfo, S.J.: Meta-learning in Distributed Data Mining Systems: Issues and Approaches. In: Kargupta, H., Chan, P. (eds.) Advances in Distributed and Parallel Knowledge Discovery, ch. 3, AAAI/MIT Press (2000)
Liu, H., Lu, H., Yao, J.: Identifying Relevant Databases for Multidatabase Mining. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 210–221 (1998)
Tsoumakas, G., Angelis, L., Vlahavas, I.: Clustering Classifiers for Knowledge Discovery from Physical Distributed Database. Data & Knowledge Engineering 49(3), 223–242 (2004)
Stolfo, S., Prodromidis, A.L., Tselepis, S., Lee, W., Fan, D.W.: JAM: Java Agents for Meta-Learning over Distributed Databases. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 74–81. AAAI Press, Menlo Park (1997)
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)
Caragea, D., Silvescu, A., Honavar, V.: A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees. International Journal of Hybrid Intelligent Systems (2003)
Czarnowski, I., Jędrzejowicz, P.: An Agent-based Algorithm for Data Reduction. In: Proc. of the Twenty-seventh SGAI International Conference on Artificial Intelligence Cambridge, England (to appear, 2007)
Merz, C.J., Murphy, M.: UCI Repository of Machine Learning Databases, Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Czarnowski, I., Jędrzejowicz, P.: Instance Reduction Approach to Machine Learning and Multi-Database Mining. In: Proceedings of the Scientific Session organized during XXI Fall Meeting of the Polish Information Processing Society, Informatica, ANNALES Universitatis Mariae Curie-Skłodowska, Lublin, pp. 60–71 (2006)
The European Network of Excellence on Intelligence Technologies for Smart Adaptive Systems (EUNITE) – EUNITE World Competition in domain of Intelligent Technologies, http://neuron.tuke.sk/competition2
Wróblewski, J.: Adaptacyjne metody klasyfikacji obiektów. PhD thesis, University of Warsaw, Warsaw (in Polish) (2001)
Krawiec, K.: Evolutionary Feature Programming. Cooperative learning for knowledge discovery and computer vision. Ph.D thesis, Poznan University of Technology, Poznan (2004)
Jędrzejowicz, P.: Social Learning Algorithm as a Tool for Solving Some Difficult Scheduling Problems. Foundation of Computing and Decision Sciences 24, 51–66 (1999)
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Czarnowski, I., Jędrzejowicz, P., Wierzbowska, I. (2008). An A-Team Approach to Learning Classifiers from Distributed Data Sources. In: Nguyen, N.T., Jo, G.S., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science(), vol 4953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78582-8_54
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DOI: https://doi.org/10.1007/978-3-540-78582-8_54
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