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Optimization of Evolutionary Instance Selection

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

Evolutionary instance selection is the most accurate process comparing to other methods based on distance, such as the instance selection methods based on k-NN. However, the drawback of evolutionary methods is their very high computational cost. We compare the performance of evolutionary and classical methods and discuss how to minimize the computational cost using optimization of genetic algorithm parameters, joining them with the classical instance selection methods and caching the information used by k-NN.

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References

  1. Antonelli, M., Ducange, P., Marcelloni, F.: Genetic training instance selection in multiobjective evolutionary fuzzy systems: a coevolutionary approach. IEEE Trans. Fuzzy Syst. 20(2), 276–290 (2012)

    Article  Google Scholar 

  2. Derrac, J., Cornelis, C., Garcia, S., Herrera, F.: Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Inf. Sci. 186, 73–92 (2012)

    Article  Google Scholar 

  3. Tsaia, C.-F., Eberleb, W., Chu, C.-Y.: Genetic algorithms in feature and instance selection. Knowl. Based Syst. 39, 240–247 (2013)

    Article  Google Scholar 

  4. Garcia, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)

    Article  Google Scholar 

  5. Olvera-López, J.A., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Kittler, J.: A review of instance selection methods. Artif. Intell. Rev. 34(2), 133–143 (2010)

    Article  Google Scholar 

  6. Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. CRC Press, Boca Raton (2013)

    Google Scholar 

  7. Jankowski, N., Grochowski, M.: Comparison of instances seletion algorithms I. Algorithms survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS, vol. 3070, pp. 598–603. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24844-6_90

    Chapter  Google Scholar 

  8. Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Mach. Learn. 38, 257–286 (2000)

    Article  MATH  Google Scholar 

  9. Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  10. Kordos, M., Blachnik, M.: Instance selection with neural networks for regression problems. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7553, pp. 263–270. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33266-1_33

    Chapter  Google Scholar 

  11. Arnaiz-González, A., Blachnik, M., Kordos, M., García-Osorio, C.: Fusion of instance selection methods in regression tasks. Inf. Fusion 30, 69–79 (2016)

    Article  Google Scholar 

  12. Kordos, M., Białka, S., Blachnik, M.: Instance selection in logical rule extraction for regression problems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 167–175. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_16

    Chapter  Google Scholar 

  13. Blachnik, M., Kordos, M.: Bagging of instance selection algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8468, pp. 40–51. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_4

    Chapter  Google Scholar 

  14. Goldberg, D.D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Boston (1989)

    MATH  Google Scholar 

  15. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)

    Book  MATH  Google Scholar 

  16. Lobo, F.G., Lima, C.F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  17. Zavoianu, Z.C., et al.: Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. Knowl. Based Syst. 87, 47–60 (2015)

    Article  Google Scholar 

  18. Cano, J.R., Herrera, F., Lozano, M.: Instance selection using evolutionary algorithms: an experimental study. In: Pal, N.R., Jain, L. (eds.) Advanced Techniques in Knowledge Discovery and Data Mining. Advanced Information and Knowledge Processing, pp. 127–152. Springer, London (2004). doi:10.1007/1-84628-183-0_5

    Google Scholar 

  19. Alcala-Fdez, J., et al.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Multiple-Valued Logic Soft Comput. 17, 255–287 (2011). http://sci2s.ugr.es/keel/datasets.php

    Google Scholar 

  20. Rusiecki, A., Kordos, M., Kamiński, T., Greń, K.: Training neural networks on noisy data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 131–142. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_13

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Automatics, University of Bielsko-Biala, Willowa 2, Bielsko-Biała, Poland

    Mirosław Kordos

Authors
  1. Mirosław Kordos
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Correspondence to Mirosław Kordos .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Kordos, M. (2017). Optimization of Evolutionary Instance Selection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59062-2

  • Online ISBN: 978-3-319-59063-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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