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Multiple parameter control for ant colony optimization applied to feature selection problem

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Abstract

The ant colony optimization algorithm (ACO) was initially developed to be a metaheuristic for combinatorial optimization problem. In scores of experiments, it is confirmed that the parameter settings in ACO have direct effects on the performance of the algorithm. However, few studies have specially reported the parameter control for ACO. The aim of this paper was to put forward some strategies to adaptively adjust the parameter in ACO and further provide a deeper understanding of ACO parameter control, including static and dynamic parameters. We choose well-known ant system (AS) and ant colony system (ACS) to be controlled by our proposed strategies. The parameters in AS and ACS include β, pheromone evaporation rate (ρ), exploration probability factor (q 0 ) and number of ants (m). We have proposed three adaptive parameter control strategies (SI, SII and SIII) based on fuzzy logic control which adjusts ρ, q 0 and m, respectively. The feature selection problem is considered for evaluating the parameter control strategies. In addition, because AS and ACS are not intrinsically fit for feature selection problem, we have modified the AS and ACS, which are named as fuzzy adaptive ant system (FAAS) and fuzzy adaptive ant colony system (FAACS), to make them more suitable for feature selection problem. Because only one parameter is allowed to be dynamically adjusted in FAAS or FAACS, the remaining parameters should be statically specified. Thus, we have developed parametric guidelines for proper combination of static parameter settings. The performance of FAAS and FAACS is compared with that of the AS-based, ACS-based, particle swarm optimization-based and genetic algorithm-based methods on a comprehensive set of 10 benchmark data sets, which are taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed algorithms outperform significantly than other methods in terms of prediction accuracy with smaller subset of features.

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References

  1. Dorigo M (1992) Optimization, learning and natural algorithms, Unpublished doctoral dissertation, Politecnico di Milano, Dipartimento diElettronica, Italy

  2. Huang P, Yin M (2014) An upper (lower) bound for Max (Min) CSP. Sci China Inf Sci 57(7):1–9

    MathSciNet  Google Scholar 

  3. Li X, Yin M (2014) Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dyn 77(1–2):61–71

    Article  MathSciNet  Google Scholar 

  4. Kirkpatrick S, Gelatt C Jr, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  5. Holland J (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, USA

    Google Scholar 

  6. Kennedy J, Eberhart R et al (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4. Perth, Australia, pp 1942–1948

  7. Dorigo M, Maniezzo V, Colorni A (2002) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

    Article  Google Scholar 

  8. Dorigo M, Gambardella L (2002) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

  9. Sttzle T, Hoos H et al (2000) MAX–MIN ant system. Future Gener Comput Syst 16(8):889–914

    Article  Google Scholar 

  10. Bullnheimer B, Hartl R, Strauss C (1997) A new rank based version of the Ant system. A computational study

  11. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theoret Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502

    Article  Google Scholar 

  13. Li X, Yin M (2013) Multiobjective binary biogeography based optimization based feature selection for gene expression data. IEEE Trans Nanobiosci 12(4):343–353

    Article  Google Scholar 

  14. Kohavi R, John G (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    Article  MATH  Google Scholar 

  15. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422

    Article  MATH  Google Scholar 

  16. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

  17. Zhu Z, Ong Y, Dash M (2007) Wrapper–filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern Part B Cybern 37(1):70–76

    Article  Google Scholar 

  18. Wang X, Yang J, Teng X, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28(4):459–471

    Article  Google Scholar 

  19. Meiri R, Zahavi J (2006) Using simulated annealing to optimize the feature selection problem in marketing applications. Eur J Oper Res 171(3):842–858

    Article  MATH  Google Scholar 

  20. Huang CL (2009) Aco-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 73(1–3):438–448

    Article  Google Scholar 

  21. Ke L, Feng Z, Ren Z (2008) An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognit Lett 29(9):1351–1357

  22. Gheyas I, Smith L (2010) Feature subset selection in large dimensionality domains. Pattern Recogn 43(1):5–13

    Article  MATH  Google Scholar 

  23. Chen Y, Miao D, Wang R (2010) A rough set approach to feature selection based on ant colony optimization. Pattern Recognit Lett 31(3):226–233

  24. Forsatia R, Moayedikiaa A, Jensenc R, Shamsfarda M, Meybodid M (2014) Enriched ant colony optimization and its application in feature selection. Neurocomputing 142:354–371

  25. Erguzel TT, Ozekes S, Gultekin S, Tarhan N (2014) Ant colony optimization based feature selection method for QEEG data classification. Psychiatry Investig 11(3):243–250

    Article  Google Scholar 

  26. Duan H, Ma G, Liu S (2008) Experimental study of the adjustable parameters in basic ant colony optimization algorithm. In: IEEE Congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 149–156

  27. Zecchin A, Simpson A, Maier H, Nixon J (2005) Parametric study for an ant algorithm applied to water distribution system optimization. IEEE Trans Evol Comput 9(2):175–191

  28. Luo X, Yu F, Zhang J (2006) Study of parametric relation in ant colony optimization approach to traveling salesman problem. Computational intelligence and bioinformatics, pp 22–32

  29. Favuzza S, Graditi G, Sanseverino E (2006) Adaptive and dynamic ant colony search algorithm for optimal distribution systems reinforcement strategy. Appl Intell 24(1):31–42

    Article  Google Scholar 

  30. Chiang CW, Huang YQ, Wang WY (2008) Ant colony optimization with parameter adaptation for multi-mode resource-constrained project scheduling. J Intell Fuzzy Syst 19(4–5):345–358

    MATH  Google Scholar 

  31. Yu WJ, Hu XM, Zhang J, Huang RZ (2009) Self-adaptive ant colony system for the traveling salesman problem. IEEE Int Conf Syst Man Cybern 1–9:1399–1404

  32. Li Y, Wang G, Chen H, Shi L, Qin L (2013) An ant colony optimization based dimension reduction method for high-dimensional datasets. J Bionic Eng 10(2):231–241

    Article  Google Scholar 

  33. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

  34. Vapnik V, Boser B, Guyon I (1992) A training algorithm for optimalmargin classifiers. In: Proceedings of the 5th annual ACM conference on computational learning theory (COLT 1992), July, 1992, pp 27–29

  35. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 2:5

  36. Lee C (1990) Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Trans Syst Man Cybern 20(2):404–418

    Article  MATH  Google Scholar 

  37. Chen Y, Lin C (2005) Combining svms with various feature selection strategies. Tech Rep [Online]. http://www.csie.ntu.edu.tw/∼cjlin/papers/features.pdf

  38. Xie J, Wang C. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Exp Syst Appl 38(5):5809–5815

Download references

Acknowledgments

This research was supported by The Chinese Government’s Executive Program “Instrumentation development and field experimentation”(SinoProbe-09), China Postdoctoral Science Foundation No. 2013M530981, National Natural Science Foundation of China No. 61303113.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, People’s Republic of China

    Gang Wang & Ying Li

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People’s Republic of China

    Gang Wang & Ying Li

  3. National Taichung University of Education, Taichung, People’s Republic of China

    HaiCheng Eric Chu

  4. College of Communication Engineering, Jilin University, Changchun, People’s Republic of China

    Yuxuan Zhang

  5. College of Physics and Electronic Information, Wenzhou University, Wenzhou, People’s Republic of China

    Huiling Chen

  6. Air Force Aviation University, Changchun, People’s Republic of China

    Weitong Hu

  7. Raytheon BBN Technologies, Boston, MA, USA

    XuJun Peng

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  1. Gang Wang
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Correspondence to Ying Li.

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Wang, G., Chu, H.E., Zhang, Y. et al. Multiple parameter control for ant colony optimization applied to feature selection problem. Neural Comput & Applic 26, 1693–1708 (2015). https://doi.org/10.1007/s00521-015-1829-8

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  • DOI: https://doi.org/10.1007/s00521-015-1829-8

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