Abstract
This paper is positioned in the area of the use of cognitive computation techniques to design intelligent systems for big data scenarios, specifically the use of evolutionary algorithms to design data-driven linguistic fuzzy rule-based systems for regression and control. On the one hand, data-driven approaches have been extensively employed to create rule bases for fuzzy regression and control from examples. On the other, adaptive defuzzification is a well-known mechanism used to significantly improve the accuracy of fuzzy systems. When dealing with large-scale scenarios, the aforementioned methods must be redesigned to allow scalability. Our proposal is based on a distributed MapReduce schema, relying on two ideas: first, a simple adaptation of a classic data-driven method to quickly obtain a set of rules, and, second, a novel scalable strategy that uses evolutionary adaptive defuzzification to achieve better behavior through cooperation among rules. Some different regression problems were used to validate our methodology through an experimental study developed and included at the end of our paper. Therefore, the proposed approach allows scalability while tackling applications of linguistic fuzzy rule-based systems for regression with adaptive defuzzification in large-scale data scenarios. This paper thus examines the use of some relevant techniques for cognitive computing when working with a vast volume of examples, a common occurrence when dealing with the design of artificial intelligent systems that perform reasoning in a similar way as humans.
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References
Siddique N, Adeli H. Nature inspired computing: an overview and some future directions. Cogn Comput. 2015;7(6):706–14.
Nobakhti A. On natural based optimization. Cogn Comput. 2010;2(2):97–119.
Wang D, Shan H, Tian Y, Liu L. Emergent face orientation recognition with internal neurons of the developmental network. Prog Artif Intell. 2018;7(4):359–67.
Dragoni M, Rospocher M. Applied cognitive computing: challenges, approaches, and real-world experiences. Prog Artif Intell. 2018;7(4):249–50.
Fan M, Zhou Q, Abel A, Fang Zheng T, Grishman R. Probabilistic belief embedding for large-scale knowledge population. Cogn Comput. 2016;8(6):1087–102.
Zhang HG, Wu L, Song Y, Su CW, Wang Q, Su F. An online sequential learning non-parametric value-at-risk model for high-dimensional time series. Cogn Comput. 2018;10(2):187–200.
Abdullah A, Hussain A, Khan IH. Introduction: dealing with big data - lessons from cognitive computing. Cogn Comput. 2015;7(6):635–6.
Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput. 2016;8(4):649–69.
Tao Z, Han B, Chen H. On intuitionistic fuzzy copula aggregation operators in multiple- attribute decision making. Cogn Comput. 2018;10(4):610–24.
Molina D, LaTorre A, Herrera F. An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions. Cogn Comput. 2018;10(4):517–44.
Pino A, Shin K, Velázquez-Rodríguez C. Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data. Prog Artif Intell. 2018;7(4):399–410.
Herrera F. Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell. 2008;1(1):27–46.
Fazzolari M, Alcalá R, Nojima Y, Ishibuchi H, Herrera F. A review of the application of multi-objective evolutionary systems: current status and further directions. IEEE Trans Fuzzy Syst. 2013;21(1):45–65.
Fernández A, López V, del Jesus MJ, Herrera F. Revisiting evolutionary fuzzy systems: taxonomy, applications, new trends and challenges. Knowl Based Syst. 2015;80:109–21.
Fernández A, Herrera F, Cordón O, del Jesus MJ, Marcelloni F. Evolutionary fuzzy systems for explainable artificial intelligence: why, when, what for, and where to? IEEE Comput Intell Mag. 2019;14(1):69–81.
Elhag S, Fernández A, Alshomrani S, Herrera F. Evolutionary fuzzy systems: a case study for intrusion detection systems. In: Bansal J, Singh P, Pal N, editors. Evolutionary and swarm intelligence algorithms. Studies in Computational Intelligence, vol. 779. Cham: Springer; 2019. p. 169–90.
Ferdaus MM, Anavatti SG, Garratt MA, Pratama M. Development of C-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. J Artif Intell Soft Com Res. 2019;9(2):99–109.
Cózar J, dela Ossa L, Gámez JA. Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach. Inform Sci. 2018;433–434:1–16.
Zikopoulos P, Eaton C, De Roos D, Deutsch T, Lapis G. Understanding big data: analytics for enterprise class Hadoop and streaming data. New York City: McGraw-Hill; 2011.
García-Pedrajas N, de Haro-García A. Scaling up data mining algorithms: review and taxonomy. Progr Artif Intell. 2012;1(1):71–87.
Río S, López V, Benítez JM, Herrera F. A MapReduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules. Int J Comp Intel Syst. 2015;8(3):422–37.
Peralta D, Río S, Ramírez-Gallego S, Triguero I, Benítez JM, Herrera F. Evolutionary feature selection for big data classification: a MapReduce approach. Math Probl Eng. 2015:501–246139.
Fernandez A, Carmona CJ, del Jesus MJ, Herrera F. A view on fuzzy systems for big data: progress and opportunities. Int J Comp Intel Syst. 2016;9(1):69–80.
Ferranti A, Segatori A, Antonelli M, Ducange P. A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data. Inf Sci. 2017;415(416):319–40.
Ducange P, Marcelloni F, Segatori A. A MapReduce-based fuzzy associative classifier for big data. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2015;1–8.
López V, del Río S, Benítez JM, Herrera F. Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data. Fuzzy Sets Syst. 2015;258:5–38.
Rodriguez-Fdez I, Mucientes M, Bugarin A. A genetic fuzzy system for large-scale regression. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). 2016; 1421–1428.
Rodriguez-Fdez I, Mucientes M, Bugarin A. SFRULER: scalable fuzzy rule learning through evolution for regression. Knowl Based Syst. 2016;110:255–66.
Rodriguez-Mier P, Mucientes M, Bugarín A. Scalable modeling of thermal dynamics in buildings using fuzzy rules for regression. In Proceedings of the IEEE International Conference on Fuzzy System (FUZZ-IEEE). 2017; 1–6.
Márquez AA, Márquez FA, Peregrín A. A scalable evolutionary linguistic fuzzy system with adaptive defuzzification in big data. In Proceedings of the IEEE International Conference on Fuzzy System (FUZZ-IEEE). 2017; 1–6.
Alcalá R, Gacto MJ, Herrera F. A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modelling in high dimensional regression problems. IEEE Trans Fuzzy Syst. 2011;19(4):666–81.
Márquez AA, Márquez FA, Roldán AM, Peregrín A. An efficient adaptive fuzzy inference system for complex and high dimensional regression problems in linguistic fuzzy modelling. Knowl Based Syst. 2013;54:42–52.
Antonelli M, Ducange P, Marcelloni F. Genetic training instance selection in multiobjective evolutionary fuzzy systems: a coevolutionary approach. IEEE Trans Fuzzy Syst. 2012;20(2):276–90.
Antonelli M, Ducange P, Marcelloni F. An efficient multi-objective evolutionary fuzzy system for regression problems. Int J Approx Reason. 2013;54(9):1434–51.
Gacto MJ, Galende M, Alcalá R, Herrera F. METSK-HDe: a multiobjective evolutionary algorithm to learn accurate tsk-fuzzy systems in high-dimensional and large scale regression problems. Inf Sci. 2014;276:63–79.
Liu P, Li H. Interval-valued intuitionistic fuzzy power Bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):494–512.
Garg H, Arora R. Dual hesitant fuzzy soft aggregation operators and their application in decision-making. Cogn Comput. 2018;10(5):769–89.
Alcala-Fdez J, Herrera F, Márquez FA, Peregrín A. Increasing fuzzy rules cooperation based on evolutionary adaptive inference systems. Int J Intell Syst. 2007;22(9):1035–64.
Márquez FA, Peregrín A, Herrera F. Cooperative evolutionary learning of linguistic fuzzy rules and parametric aggregation connectors for Mamdani fuzzy system. IEEE Trans Fuzzy Syst. 2007;15(6):168–1178.
Cordón O, Herrera F, Márquez FA, Peregrín A. A study on the evolutionary adaptive defuzzification methods in fuzzy modelling. Int J Hybrid Intell Syst. 2004;1(1):36–48.
Wang L, Mendel J. Generating fuzzy rules by learning from examples. IEEE Trans Syst, Man, Cybern. 1992;22(6):1414–27.
Ramirez-Gallego S, Fernández A, García S, Chen M, Herrera F. Big data: tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Inf Fusion. 2018;42:51–61.
Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, et al. Apache spark: a unified engine for big data processing. Commun ACM. 2016;59(11):56–65.
Demšar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006;7:1–30.
García S, Herrera F. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. J Mach Learn Res. 2008;9:2579–96.
Cho JS, Park DJ. Novel fuzzy logic control based on weighting of partially inconsistent rules using neural network. J Intel Fuzzy Syst. 2000;8:99–100.
Laney D. 3D data management: controlling data volume, velocity and variety. META Group Research Note 6. 2001; 70.
Fernández A, del Río S, López V, Bawakid A, del Jesus MJ, Benítez JM, et al. Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 2014;4(5):380–409.
White T. Hadoop: the definitive guide. Sebastopol: O’Reilly Media, Inc.; 2012.
Dean J, Ghemawat S. MapReduce: a flexible data processing tool. Commun ACM. 2010;53(1):72–7.
Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, et al. Pregel: a system for large-scale graph processing. In Proceedings of the ACM SIGMOD International Conference on Management of Data 2010;135–146.
Padillo F, Luna JM, Ventura S. Exhaustive search algorithms to mine subgroups on big data using Apache Spark. Prog Artif Intell. 2017;6(2):145–58.
Pulgar-Rubio F, Rivera-Rivas AJ, Pérez-Godoy MD, González P, Carmona CJ, del Jesus MJ. MEFASD-BD: multi-objective evolutionary algorithm for subgroup discovery in big data environments - a MapReduce solution. Knowl Based Syst. 2017;117:70–8.
Arnaiz-González A, González-Rogel A, Díez-Pastor JF, López-Nozal C. MR-DIS: democratic instance selection for big data by MapReduce. Prog Artif Intell. 2017;6(3):211–9.
Luna-Romera JM, García-Gutiérrez J, Martínez-Ballesteros M, Riquelme JC. An approach to validity indices for clustering techniques in big data. Prog Artif Intell. 2018;7(2):81–94.
Eshelman LJ. The CHC adaptive search algorithm: how to safe search when engaging in nontraditional genetic recombination. In G.J.E. Rawlings (Ed.), Foundations of genetic algorithms. 1991;1:265–283.
Herrera F, Lozano M, Sánchez A. A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study. Int J Intell Syst. 2003;18:309–38.
Alcala-Fdez J, Sánchez L, García S, del Jesus M, Ventura S, Garrell J, et al. Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 2009;13(3):307–18.
Sheskin D. Handbook of parametric and nonparametric statistical procedures. Boca Raton: Chapman & Hall/CRC; 2006.
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This work was supported by grant from the Spanish Ministry of Science under project TIN2017-89517-P.
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López, S., Márquez, A.A., Márquez, F.A. et al. Evolutionary Design of Linguistic Fuzzy Regression Systems with Adaptive Defuzzification in Big Data Environments. Cogn Comput 11, 388–399 (2019). https://doi.org/10.1007/s12559-019-09632-4
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DOI: https://doi.org/10.1007/s12559-019-09632-4