Abstract
The classification tries to assign the best category to given unknown records based on previous observations. It is clear that with the growing amount of data, any classification algorithm can be very slow. The learning speed of many developed state-of-the-art algorithms like deep neural networks or support vector machines is very low. Evolutionary-based approaches in classification have the same problem. This paper describes five different evolutionary-based approaches that solve the classification problem and run in real time. This was achieved by using GPU parallelization. These classifiers are evaluated on two collections that contains millions of records. The proposed parallel approach is much faster and preserve the same precision as a serial version.
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References
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An evaluation of naive bayesian anti-spam filtering. arXiv preprint arXiv:cs/0006013 (2000)
Baldi, P., Sadowski, P., Whiteson, D.: Searching for exotic particles in high-energy physics with deep learning. Nat. Commun. 5 (2014)
Brun, C., Chevenet, F., Martin, D., Wojcik, J., Guénoche, A., Jacq, B., et al.: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol. 5(1), R6 (2004)
Cano, A., Zafra, A., Ventura, S.: Solving classification problems using genetic programming algorithms on gpus. In: Hybrid Artificial Intelligence Systems, pp. 17–26. Springer (2010)
Cano, A., Zafra, A., Ventura, S.: A parallel genetic programming algorithm for classification. In: Hybrid Artificial Intelligent Systems, pp. 172–181. Springer (2011)
Cano, A., Zafra, A., Ventura, S.: Speeding up the evaluation phase of gp classification algorithms on gpus. Soft Comput. 16(2), 187–202 (2012)
Deng, L., Yu, D.: Deep learning: Methods and applications. Found. Trends Signal Process. 7(34), 197–387 (2013). http://dx.doi.org/10.1561/2000000039
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS ’95, pp. 39–43, 4–6 Oct 1995
Hagan, M.T., Demuth, H.B., Beale, M.H., et al.: Neural Network Design. Pws Publishers, Boston (1996)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision, vol. 5. McGraw-Hill, New York (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Nov–Dec 1995
Koza, J.R.: Genetic Programming: On the Programming of Computers By Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)
Manikandan, P., Selvarajan, S.: Data Clustering Using Cuckoo Search Algorithm (CSA), pp. 1275–1283 (2012)
Mori, S., Nishida, H., Yamada, H.: Optical Character Recognition. Wiley, New York (1999)
Platos, J., Snasel, V., Jezowicz, T., Kromer, P., Abraham, A.: A pso-based document classification algorithm accelerated by the cuda platform. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1936–1941 (2012)
Sarkar, B.K., Chakraborty, S.K.: Classification system using parallel genetic algorithm. Int. J. Innov. Comput. Appl. 3(4), 223–241 (2011)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002). http://doi.acm.org/10.1145/505282.505283
Srivatsava, P.R., Mallikarjun, B., Yang, X.S.: Optimal test sequence generation using firefly algorithm. Swarm Evol. Comput. 8(2013), 4453 (2012)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1958–1970 (2008)
Wang, S.C.: Artificial neural network. In: Interdisciplinary Computing in Java Programming, pp. 81–100. Springer, New York (2003)
Wang, Z., Zhang, Q., Zhang, D.: A pso-based web document classification algorithm. In: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD ’07, vol. 03, pp. 659–664. IEEE Computer Society, Washington, D.C., (2007). http://dx.doi.org/10.1109/SNPD.2007.84
Yang, X.S., Deb, S.: Cuckoo search via lvy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 210–214 (2009)
Yang, X.S.: Nature-inspired Metaheuristic Algorithms, 2nd edn, pp. 81–89. Luniver Press, Frome (2010)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization, pp. 65–74 (2010)
Yang, X.S.: Flower pollination algorithm for global optimization. In: Unconventional Computation and Natural Computation, pp. 240–250. Springer, Berlin (2012)
Yang, X.S.: Nature-Inspired Optimization Algorithms. School of Science and Technology, Middlesex University, London (2014)
Zhou, S., Nittoor, P.R., Prasanna, V.K.: High-performance traffic classification on gpu. In: IEEE 26th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) IEEE, pp. 97–104 (2014)
Zhu, L., Jin, H., Zheng, R., Feng, X.: Effective naive bayes nearest neighbor based image classification on GPU. J. Supercomput. 68(2), 820–848 (2014)
Acknowledgments
This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/105 “DPDM—Database of Performance and Dependability Models” of the Student Grand System, VŠB—Technical University of Ostrava and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB—Technical University of Ostrava.
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Ježowicz, T., Buček, P., Platoš, J., Snášel, V. (2016). Evolutionary Algorithms for Fast Parallel Classification. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_62
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