Abstract
The C–fuzzy random forest is a novel ensemble classifier which uses C-fuzzy decision trees as unit classifiers. The main problem connected with this classifier is a relatively long learning process time. In this paper the method of reducing the C–fuzzy random forest’s learning time is proposed. Authors proposed and described the method of parallelization of this classifier’s learning process by generating trees which are the parts of the forest in separate threads. The experiments which were designed to check the effectiveness of the proposed method were performed and the results were presented and discussed.
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Notes
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There is also another decision–making strategy which assumes making the single decision by the whole forest. It is described in [2].
References
Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Diaz-Valladares, R.A.: A fuzzy random forest: Fundamental for design and construction. In: Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge- Based Systems (IPMU 2008), pp. 1231–1238 (2008)
Bonissone, P., Cadenas, J.M., Carmen Garrido, M., Andres Diaz-Valladares, R.: A fuzzy random forest. Int. J. Approx. Reason. 51(7), 729–747 (2010)
Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Diaz-Valladares, R.A.: Combination methods in a Fuzzy Random Forest. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, pp. 1794–1799, October 2008
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Chen, J., et al.: A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 28(4), 919–933 (2017)
Gadomer, Ł., Sosnowski, Z.A.: Fuzzy random forest with c–fuzzy decision trees. In: Saeed, K., Homenda, W. (eds.) CISIM 2016. LNCS, vol. 9842, pp. 481–492. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45378-1_43
Gadomer, Ł., Sosnowski, Z.A.: Knowledge aggregation in decision-making process with c-fuzzy random forest using OWA operators. Soft Comput., 1–15 (2018)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. Part B Cybern. 28(1), 1–14 (1998)
Jurczuk, K., Czajkowski, M., Kretowski, M.: Evolutionary induction of a decision tree for large-scale data: a GPU-based approach. Soft Comput. 21(24), 7363–7379 (2017)
Jurczuk, K., Kretowski, M., Bezy-Wendling, J.: GPU-based computational modeling of magnetic resonance imaging of vascular structures. Int. J. High Perform. Comput. Appl
Kumar, V.: Introduction to Parallel Computing, 2nd edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2002)
Lichman, M.: UCI machine learning repository (2013)
Limprasert, W.: Parallel random forest with IPython cluster. In: 2015 International Computer Science and Engineering Conference (ICSEC), pp. 1–6, November 2015
Patterson, D.A., Hennessy, J.L.: Computer Organization and Design. The Hardware/Software Interface, 5th edn. Morgan Kaufmann Publishers Inc., San Francisco (2013)
Pedrycz, W., Sosnowski, Z.A.: C-fuzzy decision trees. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35(4), 498–511 (2005)
Acknowledgment
This work was supported by the grant S/WI/3/2018 from Bialystok University of Technology founded by Ministry of Science and Higher Education.
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Gadomer, Ł., Sosnowski, Z.A. (2018). Parallel C–Fuzzy Random Forest. In: Saeed, K., Homenda, W. (eds) Computer Information Systems and Industrial Management. CISIM 2018. Lecture Notes in Computer Science(), vol 11127. Springer, Cham. https://doi.org/10.1007/978-3-319-99954-8_22
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