Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)

Abstract

The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) — often through guided feature reductions or parameter optimizations.

In this paper we introduce PolyACO: A novel Ant Colony based classifier operating in two dimensional space that utilizes ray casting. To the best of our knowledge, our work is the first reported Ant Colony based classifier which is non-hybrid, in the sense, that it does not build on any legacy classifiers. The essence of the scheme is to create a separator in the feature space by imposing ant-guided random walks in a grid system. The walks are self-enclosing so that the ants return back to the starting node forming a closed classification path yielding a many edged polygon. Experimental results on both synthetic and real-life data show that our scheme is able to perfectly separate both simple and complex patterns, without utilizing “kernel tricks” and outperforming existing classifiers, such as polynomial and linear SVM. The results are impressive given the simplicity of PolyACO compared to other approaches such as SVM.

References

  1. 1.
    Asmar, D., Elshamli, A., Areibi, S.: A comparative assessment of ACO algorithms within a TSP environment. Dyn. Continous Discrete Impulsive Syst.-Ser. B-Appl. Algorithms 1, 462–467 (2005)Google Scholar
  2. 2.
    Chan, A., Freitas, A.A.: A new classification-rule pruning procedure for an ant colony algorithm. In: Talbi, E.-G., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds.) EA 2005. LNCS, vol. 3871, pp. 25–36. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Daly, R., Shen, Q.: Learning Bayesian Network Equivalence Classes with Ant Colony Optimization (2014). arXiv preprint arXiv:1401.3464
  4. 4.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  5. 5.
    Gutjahr, W.J.: ACO algorithms with guaranteed convergence to the optimal solution. Inf. Process. Lett. 82(3), 145–153 (2002)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Hota, S., Satapathy, P., Jagadev, A.K.: Modified ant colony optimization algorithm (MAnt-Miner) for classification rule mining. In: Jain, L.C., Patnaik, S., Ichalkaranje, N. (eds.) Intelligent Computing, Communication and Devices, pp. 267–275. Springer, New Delhi (2015)CrossRefGoogle Scholar
  7. 7.
    Junior, I.C.: Data mining with ant colony algorithms. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.) ICIC 2013. LNCS, vol. 7996, pp. 30–38. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)CrossRefGoogle Scholar
  9. 9.
    Lian, T.A., Llave, M.R., Goodwin, M., Bouhmala, N.: Towards multilevel ant colony optimisation for the Euclidean symmetric traveling salesman problem. In: Ali, M., Kwon, Y.S., Lee, C.-H., Kim, J., Kim, Y. (eds.) IEA/AIE 2015. LNCS, vol. 9101, pp. 222–231. Springer, Heidelberg (2015)Google Scholar
  10. 10.
    Liu, B., Abbas, H., McKay, B.: Classification rule discovery with ant colony optimization. In: IEEE/WIC International Conference on Intelligent Agent Technology, IAT 2003, pp. 83–88. IEEE (2003)Google Scholar
  11. 11.
    Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012)CrossRefGoogle Scholar
  12. 12.
    Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Trans. Evol. Comput. 11(5), 651–665 (2007)CrossRefGoogle Scholar
  13. 13.
    Neumann, F., Sudholt, D., Witt, C.: Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intell. 3(1), 35–68 (2009)CrossRefGoogle Scholar
  14. 14.
    Parpinelli, R.S., Lopes, H.S., Freitas, A., et al.: Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6(4), 321–332 (2002)CrossRefMATHGoogle Scholar
  15. 15.
    Roth, S.D.: Ray casting for modeling solids. Comput. Graph. Image Process. 18(2), 109–144 (1982)CrossRefGoogle Scholar
  16. 16.
    Salama, K.M., Abdelbar, A.M.: Learning neural network structures with ant colony algorithms. Swarm Intell. 1–37, 229–265 (2015)CrossRefGoogle Scholar
  17. 17.
    Salama, K.M., Freitas, A.A.: Ant colony algorithms for constructing Bayesian multi-net classifiers. Intell. Data Anal. 19(2), 233–257 (2015)Google Scholar
  18. 18.
    Stützle, T., Hoos, H.: MAX-MIN Ant System and local search for the traveling salesman problem. In: IEEE International Conference on Evolutionary Computation, 1997, pp. 309–314. IEEE (1997)Google Scholar
  19. 19.
    Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)CrossRefMATHGoogle Scholar
  20. 20.
    Stützle, T., López-Ibáñez, M., Dorigo, M.: A concise overview of applications of ant colony optimization. Wiley Encycl. Oper. Res. Manage. Sci. 26(2), 25–27 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Deptartment of ICT, Institute for Technology and SciencesUniversity of AgderAgderNorway
  2. 2.Department of Computer ScienceAkershus University College of Applied SciencesOsloNorway

Personalised recommendations