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.
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Notes
- 1.
The hyperplane is a line in a two-dimensional space.
- 2.
Note that s is one of the possible polygons with the shortest circumference that is able to perfectly separate the data. The reason for this is explained in Sect. 3.3.
- 3.
Many more data sets where tested, but due to the limited space in the paper only the most interesting results are included.
- 4.
References
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)
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)
Daly, R., Shen, Q.: Learning Bayesian Network Equivalence Classes with Ant Colony Optimization (2014). arXiv preprint arXiv:1401.3464
Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)
Gutjahr, W.J.: ACO algorithms with guaranteed convergence to the optimal solution. Inf. Process. Lett. 82(3), 145–153 (2002)
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)
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)
Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 11(1), 652–657 (2011)
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)
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)
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)
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)
Neumann, F., Sudholt, D., Witt, C.: Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intell. 3(1), 35–68 (2009)
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)
Roth, S.D.: Ray casting for modeling solids. Comput. Graph. Image Process. 18(2), 109–144 (1982)
Salama, K.M., Abdelbar, A.M.: Learning neural network structures with ant colony algorithms. Swarm Intell. 1–37, 229–265 (2015)
Salama, K.M., Freitas, A.A.: Ant colony algorithms for constructing Bayesian multi-net classifiers. Intell. Data Anal. 19(2), 233–257 (2015)
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)
Stützle, T., Hoos, H.H.: MAX-MIN ant system. Future Gener. Comput. Syst. 16(8), 889–914 (2000)
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)
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Goodwin, M., Yazidi, A. (2016). Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_5
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