Abstract
The paper proposes an agent-based approach to the multiple-objective selection of reference vectors from original datasets. Effective and dependable selection procedures are of vital importance to machine learning and data mining. The suggested approach is based on the multiple agent paradigm. The authors propose using JABAT middleware as a tool and the original instance reduction procedure as a method for selecting reference vectors under multiple objectives. The paper contains a brief introduction to the multiple objective optimization, followed by the formulation of the multiple-objective, agent-based, reference vectors selection optimization problem. Further sections of the paper provide details on the proposed algorithm generating a non-dominated (or Pareto-optimal) set of reference vector sets. To validate the approach the computational experiment has been planned and carried out. Presentation and discussion of experiment results conclude the paper.
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References
Bellifemine, F., Caire, G., Poggi, A., Rimassa, G.: JADE. A White Paper. Exp 3(3), 6–20 (2003)
Talukdar, S., Baerentzen, L., Gove, L., de Souza, P.: Asynchronous Teams: Co-operation Schemes for Autonomous, Computer-Based Agents, Technical Report EDRC 18-59-96, Carnegie Mellon University, Pittsburgh (1996)
Jȩdrzejowicz, P., Wierzbowska, I.: JADE-Based A-Team Environment, pp. 719–726. Springer, Berlin, Heidelberg (2006)
Oster, G.F., Wilson, E.O.: Caste and Ecology in the Social Insect, vol. 8. Princeton University Press, Princeton, NJ (1978)
Davis, L. (ed.): Handbook of Genetic Algorithms. Van Nostrand, Reinhold (1991)
Glover, F.: Tabu Search. Part I and II, ORSA Journal of Computing. 1(3), Summer (1990) and 2(1) Winter (1990)
Kennedy, J., Eberhart, R.C.: Particle swarm optimisation. In: Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942-1948 (1995)
Merz, C.J., Murphy, P.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
Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithm. Machine Learning 33(3), 33–33 (2000)
Duch, W., Blachnik, M., Wieczorek, T.: Probabilistic distance measure for prototype-based rules. In: Proc. of the 12 International Conference on Neural Information Processing, ICONIP, pp. 445–450 (2005)
Jaszkiewicz, A.: Multiple objective metaheuristic algorithms for combinational optimization. Habilitation thesis, 360, Pozna University of Technology, Poznań (2001)
Czarnowski, I., Jȩdrzejowicz, P.: An Approach to instance reduction in supervised learning. In: Coenen, F., Preece, A., Macintosh, A. (eds.) Research and Development in Intelligent Systems, vol. XX, pp. 267–282. Springer, London (2004)
Stefanowski, J.: Algorytmy indukcji regu decyzyjnych w odkrywaniu wiedzy, Habilitation thesis, 361, Pozna University of Technology, Poznań (in Polish) (2001)
Quilan, J.R.: C 4.5: programs for machine learning, San Matoe. Morgan Kaufman, Seattle (1993)
Breiman, L., Friedman, J.H., Oslhen, R.A., Stone, C.J.: Classification and Regression Trees. Belmont, CA: Wadsworth International Group (1984)
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Czarnowski, I., Jȩdrzejowicz, P. (2007). An Agent-Based Approach to the Multiple-Objective Selection of Reference Vectors. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_10
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DOI: https://doi.org/10.1007/978-3-540-73499-4_10
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