Abstract
The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Two comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single- objective approach.
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References
Kuncheva, L.I., Bezdek, J.C.: Nearest prototype classification clustering, genetic algorithms, or random search? IEEE Trans. on Systems, Man and Cybernetics-Part C 28(1), 160–164 (1998)
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. on Evolutionary Computation 4(2), 164–171 (2000)
Cano, J.R., Herrera, F., Lozano, M.: Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Trans. on Evolutionary Computation 7(6), 561–575 (2003)
Ho, S.-Y., Liu, C.-C., Liu, S.: Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters 23(13), 1495–1503 (2002)
Kuncheva, L.I., Jain, L.C.: Nearest neighbor classifier: Simultaneous editing and feature selection. Pattern Recognition Letters 20, 1149–1156 (1999)
Ho, S.-Y., Chang, X.-I.: An efficient generalized multiobjective evolutionary algorithm. In: Proc. of the Genetic and Evolutionary Computation Conference GECCO 1999, Orlando, July 1999, pp. 871–878 (1999)
Chen, J.-H., Ho, S.-Y.: Evolutionary multi-objective optimization of flexible manufacturing systems. In: Proc. of the Genetic and Evolutionary Computation Conference GECCO 2001, San Francisco, July 2001, pp. 1260–1267 (2001)
Ho, S.-Y., Chen, Y.-C.: An efficient evolutionary algorithm for accurate polygonal approximation. Pattern Recognition 34, 2305–2317 (2001)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strengthen Pareto approach. IEEE Trans. on Evolutionary Computation 3(4), 257–271 (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffman, San Mateo (1993)
Wilson, R.D., Martinez, T.R.: Reduction Techniques for Instance-based Learning Algorithms. Machine Learning 38, 257–286 (2000)
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases (1998), Available from http://www.ics.uci.edu/~mlearn/MLRepository.html
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© 2004 Springer-Verlag Berlin Heidelberg
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Chen, JH., Chen, HM., Ho, SY. (2004). Design of Nearest Neighbor Classifiers Using an Intelligent Multi-objective Evolutionary Algorithm. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_29
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DOI: https://doi.org/10.1007/978-3-540-28633-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
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