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Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

This paper presents a new wrapper method able to optimize simultaneously the parameters of the classifier while the size of the subset of features that better describe the input dataset is also being minimized. The search algorithm used for this purpose is based on a co-evolutionary algorithm optimizing several objectives related with different desirable properties for the final solutions, such as its accuracy, its final number of features, and the generalization ability of the classifier. Since these objectives can be sorted according to their priorities, a lexicographic approach has been applied to handle this many-objective problem, which allows the use of a simple evolutionary algorithm to evolve each one of the different sub-populations.

This work was supported by projects TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”) and PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), and by European Regional Development Funds (ERDF).

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References

  1. Ben-Tal, A.: Characterization of Pareto and lexicographic optimal solutions. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making Theory and Application. Lecture Notes in Economics and Mathematical System, vol. 177, pp. 1–11. Springer, Berlin (1979). https://doi.org/10.1007/978-3-642-48782-8_1

    Chapter  Google Scholar 

  2. Caruana, R., Freitag, D.: Greedy attribute selection. In: Cohen, W.W., Hirsh, H. (eds.) Proceedings of the Eleventh International Conference on International Conference on Machine Learning, ICML 1994, pp. 28–36. Morgan Kaufmann, New Brunswick, July 1994

    Chapter  Google Scholar 

  3. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

    Article  Google Scholar 

  4. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measure. 20(1), 37–46 (1960). https://doi.org/10.1037/h0026256

    Article  Google Scholar 

  5. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley Interscience Series in Systems and Optimization. Wiley, Chichester (2001)

    MATH  Google Scholar 

  6. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(2), 115–148 (1995). https://www.complex-systems.com/abstracts/v09_i02_a02/

  7. Deb, K., Agrawal, S.: A niched-penalty approach for constraint handling in genetic algorithms. In: Dobnikar, A., Steele, N.C., Pearson, D.W., Albrecht, R.F. (eds.) Artificial Neural Networks and Genetic Algorithms, pp. 235–243. Springer, Portorož (1999). https://doi.org/10.1007/978-3-7091-6384-9_40

    Chapter  Google Scholar 

  8. Dheeru, D., Karra Taniskidou, E.: UCI machine learning repository. School of Information and Computer Sciences, University of California, Irvine, CA, USA (2017). http://archive.ics.uci.edu/ml

  9. Drechsler, N., Sülflow, A., Drechsler, R.: Incorporating user preferences in many-objective optimization using relation \(\epsilon \)-preferred. Natural Comput. 14(3), 469–483 (2015). https://doi.org/10.1007/s11047-014-9422-0

    Article  MathSciNet  Google Scholar 

  10. Farina, M., Amato, P.: On the optimal solution definition for many-criteria optimization problems. In: Keller, J., Nasraoui, O. (eds.) Proceedings of the 2002 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 233–238. IEEE, New Orleans, June 2002. https://doi.org/10.1109/NAFIPS.2002.1018061

  11. Forsyth, R.S.: Zoo dataset. Mapperley Park, Nottingham, UK (1990). https://archive.ics.uci.edu/ml/datasets/Zoo

  12. González, J., Ortega, J., Damas, M., Martín-Smith, P., Gan, J.Q.: A new multi-objective wrapper method for feature selection - accuracy and stability analysis for BCI. Neurocomputing 333(14), 407–418 (2019). https://doi.org/10.1016/j.neucom.2019.01.017

    Article  Google Scholar 

  13. Gutlein, M., Frank, E., Hall, M., Karwath, A.: Large-scale attribute selection using wrappers. In: Smith-Miles, K., Keogh, E., Lee, V.C. (eds.) Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009. IEEE, Nashville, March 2009. https://doi.org/10.1109/CIDM.2009.4938668

  14. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, ICNN 1995, vol. 6, pp. 1942–1948. IEEE, Perth, November 1995. https://doi.org/10.1109/ICNN.1995.488968

  15. Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: Arai, K., Mellouk, A. (eds.) Proceedings of the 2014 Science and Information Conference, pp. 372–378. The Science and Information (SAI) Organization, London, August 2014. https://doi.org/10.1109/SAI.2014.6918213

  16. Khare, V.R., Yao, X., Sendhoff, B.: Credit assignment among neurons in co-evolving populations. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 882–891. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_89

    Chapter  Google Scholar 

  17. Khosravani, S., Jalali, M., Khajepour, A., Kasaiezadeh, A., Chen, S.K., Litkouhi, B.: Application of lexicographic optimization method to integrated vehicle control systems. IEEE Trans. Ind. Electron. 65(12), 9677–9686 (2018). https://doi.org/10.1109/TIE.2018.2821625

    Article  Google Scholar 

  18. Klepikova, M.G.: The stability of lexicographic optimization problem. USSR Comput. Math. Math. Phys. 25(1), 21–29 (1985). https://doi.org/10.1016/0041-5553(85)90037-0

    Article  MathSciNet  MATH  Google Scholar 

  19. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997). https://doi.org/10.1016/S0004-3702(97)00043-X

    Article  MATH  Google Scholar 

  20. Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), 13 (2015). https://doi.org/10.1145/2792984

    Article  Google Scholar 

  21. Luke, S., et al.: ECJ 26. A Java-based evolutionary computation research system. https://cs.gmu.edu/~eclab/projects/ecj/

  22. Madonado, S., Weber, R.: A wrapper method for feature selection using support vector machines. Inf. Sci. 179(13), 2208–2217 (2009). https://doi.org/10.1016/j.ins.2009.02.014

    Article  Google Scholar 

  23. Marill, T., Green, D.: On the effectiveness of receptors in recognition systems. IEEE Trans. Inf. Theory 9(1), 11–17 (1963). https://doi.org/10.1109/TIT.1963.1057810

    Article  Google Scholar 

  24. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin (1998)

    MATH  Google Scholar 

  25. Podinovskii, V.V., Gavrilov, V.M.: Optimization with respect to successive criteria (Optimizatsiya po posledovatel’no primenyaemym kriteriyam). Sovetskoe Radio, Moscow, Russia (1975)

    Google Scholar 

  26. Popovici, E., Bucci, A., Wiegand, R.P., De Jong, E.D.: Coevolutionary principles. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 987–1033. Springer, Berlin (2012). https://doi.org/10.1007/978-3-540-92910-9_31

    Chapter  Google Scholar 

  27. Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000). https://doi.org/10.1162/106365600568086

    Article  Google Scholar 

  28. Rasekhipour, Y., Fadakar, I., Khajepour, A.: Autonomous driving motion planning with obstacles prioritization using lexicographic optimization. Control Eng. Pract. 77, 235–246 (2018). https://doi.org/10.1016/j.conengprac.2018.04.014

    Article  Google Scholar 

  29. Schmiedle, F., Drechsler, N., Große, D., Drechsler, R.: Priorities in multi-objective optimization for genetic programming. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.M. (eds.) Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001, pp. 129–136. Morgan Kaufmann Publishers Inc., San Francisco, July 2001. https://dl.acm.org/citation.cfm?id=2955256

  30. Whitney, A.W.: A direct method of nonparametric measurement selection. EEE Trans. Comput. C–20(9), 1100–1103 (1971). https://doi.org/10.1109/T-C.1971.223410

    Article  MATH  Google Scholar 

  31. Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: Abbass, H., Essam, D., Sarker, R. (eds.) Proceedings of the 2012 IEEE Congress on Evolutionary Computation, CEC 2012. IEEE, Brisbane, June 2012. https://doi.org/10.1109/CEC.2012.6256617

  32. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013). https://doi.org/10.1109/TSMCB.2012.2227469

    Article  Google Scholar 

  33. Zykina, A.V.: A lexicographic optimization algorithm. Autom. Remote Control 65(3), 363–368 (2004). https://doi.org/10.1023/B:AURC.0000019366.84601.8e

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain

    Jesús González, Julio Ortega, Miguel Damas & Pedro Martín-Smith

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  1. Jesús González
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  2. Julio Ortega
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  3. Miguel Damas
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  4. Pedro Martín-Smith
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Correspondence to Jesús González .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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González, J., Ortega, J., Damas, M., Martín-Smith, P. (2019). Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_39

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-20518-8

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