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Hybrid Optimization Method Applied to Adaptive Splitting and Selection Algorithm

  • Pedro Lopez-GarciaEmail author
  • Michał Woźniak
  • Enrique Onieva
  • Asier Perallos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

The paper presents an approach to train combined classifiers based on feature space splitting and selection of the best classifier ensemble to each subspace of feature space. The learning method uses a hybrid algorithm that combines a Genetic Algorithm and Cross Entropy Method. The proposed approach was evaluated on the basis of the comprehensive computer experiments run on balanced and imbalanced datasets, and compared with Cluster and Selection algorithm, improving the results obtained by this technique.

Keywords

Genetic algorithms Cross entropy Classification Machine learning Classifier ensemble Hybrid optimization 

Notes

Acknowledgment

Pedro Lopez-Garcia, Enrique Onieva and Asier Perallos’ work was supported by TIMON Project (Enhanced real time services for an optimized multimodal mobility relying on cooperative networks and open data). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 636220. Also, Michal Wozniak’s work was supported by the Polish National Science Centre under the grant no. DEC-2013/09/B/ST6/02264 and by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.

References

  1. 1.
    Akhand, M.A.H., Murase, K.: Ensembles of neural networks based on the alteration of input feature values. Int. J. Neural Syst. 22(1), 77–87 (2012)CrossRefGoogle Scholar
  2. 2.
    Bäck, T., Schwefel, H.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)CrossRefGoogle Scholar
  3. 3.
    Brown, G., Wyatt, J.L., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5–20 (2005)CrossRefGoogle Scholar
  4. 4.
    Caballero, R., Hernndez-Daz, A.G., Laguna, M., Molina, J.: Cross entropy for multiobjective combinatorial optimization problems with linear relaxations. Eur. J. Oper. Res. 243(2), 362–368 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263–286 (1995)zbMATHGoogle Scholar
  6. 6.
    Giacinto, G., Roli, F.: Dynamic classifier selection based on multiple classifier behaviour. Pattern Recogn. 34(9), 1879–1881 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)MathSciNetGoogle Scholar
  8. 8.
    Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artif. Intell. Rev. 12(4), 265–319 (1998)CrossRefzbMATHGoogle Scholar
  9. 9.
    Jackowski, K., Wozniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Anal. Appl. 12(4), 415–425 (2009)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. Adv. Neural Inf. Process. Syst. 7, 231–238 (1995)Google Scholar
  12. 12.
    L.I. Kuncheva. Clustering-and-selection model for classifier combination. In: Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, Proceedings. vol. 1, pp. 185–188 (2000)Google Scholar
  13. 13.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Piscataway (2004)CrossRefzbMATHGoogle Scholar
  14. 14.
    Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans. Intell. Transp. Syst. 17(2), 557–569 (2016)CrossRefGoogle Scholar
  15. 15.
    Onieva, E., Naranjo, J.E., Milanés, V., Alonso, J., García, R., Pérez, J.: Automatic lateral control for unmanned vehicles via genetic algorithms. Appl. Soft Comput. 11(1), 1303–1309 (2011)CrossRefGoogle Scholar
  16. 16.
    Osaba, E., Diaz, F., Onieva, E.: Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl. Intell. 41(1), 145–166 (2014)CrossRefGoogle Scholar
  17. 17.
    Rastrigin, L.A., Erenstein, R.H.: Method of Collective Recognition. Energoizdat, Moscow (1981)Google Scholar
  18. 18.
    Rubinstein, R.: The cross-entropy method for combinatorial and continuous optimization. Methodol. Comput. Appl. Probab. 1(2), 127–190 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Wozniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). Special Issue on Information Fusion in Hybrid Intelligent Fusion SystemsCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pedro Lopez-Garcia
    • 1
    Email author
  • Michał Woźniak
    • 2
  • Enrique Onieva
    • 1
  • Asier Perallos
    • 1
  1. 1.Deusto Institute of Technology (DeustoTech)University of DeustoBilbaoSpain
  2. 2.Department of Systems and Computer NetworksWrocław University of TechnologyWrocławPoland

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