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2018 | OriginalPaper | Chapter

Ensemble and Fuzzy Techniques Applied to Imbalanced Traffic Congestion Datasets: A Comparative Study

Authors : Pedro Lopez-Garcia, Antonio D. Masegosa, Enrique Onieva, Eneko Osaba

Published in: Bioinspired Optimization Methods and Their Applications

Publisher: Springer International Publishing

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Abstract

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets.

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López, V., Fernández, A., Moreno-Torres, J.G., Herrera, F.: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Syst. Appl. 39, 6585–6608 (2012)CrossRef

Sardari, S., Eftekhari, M.: A fuzzy decision tree approach for imbalanced data classification, pp. 292–297 (2016)

Savetratanakaree, K., Sookhanaphibarn, K., Intakosum, S., Thawonmas, R.: Borderline over-sampling in feature space for learning algorithms in imbalanced data environments. IAENG Int. J. Comput. Sci. 43, 363–373 (2016)

Lopez-Garcia, P., Onieva, E., Osaba, E., Masegosa, A.D., Perallos, A.: A hybrid method for short-term traffic congestion forecasting using genetic algorithms and cross entropy. IEEE Trans. Intell. Transp. Syst. 17, 557–569 (2016)CrossRef

Guo, L., Ge, P.S., Zhang, M.H., Li, L.H., Zhao, Y.B.: Pedestrian detection for intelligent transportation systems combining adaboost algorithm and support vector machine. Expert Syst. Appl. 39, 4274–4286 (2012)CrossRef

Cervantes, J., Li, X., Yu, W.: Imbalanced data classification via support vector machines and genetic algorithms. Connect. Sci. 26, 335–348 (2014)CrossRef

Xu, Z., Watada, J., Wu, M., Ibrahim, Z., Khalid, M.: Solving the imbalanced data classification problem with the particle swarm optimization based support vector machine. IEEJ Trans. Electron. Inf. Syst. 134, 788–795 (2014)

Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach. Learn. 36, 105–139 (1999)CrossRef

Fang, Y., Fu, Y., Sun, C., Zhou, J.: Improved boosting algorithm using combined weak classifiers. J. Comput. Inf. Syst. 7, 1455–1462 (2011)

10.

Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010)CrossRef

11.

Nama, S., Saha, A.: An ensemble symbiosis organisms search algorithm and its application to real world problems. Decis. Sci. Lett. 7, 103–118 (2018)CrossRef

12.

Zhao, Z., Liu, Y., Li, J., Wang, J., Wang, X.: A study of fuzzy clustering ensemble algorithm focusing on medical data analysis. In: Yen, N., Hung, J. (eds.) FC 2016. LNEE, vol. 422, pp. 383–396. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3187-8_37CrossRef

13.

Pescaru, D., Curiac, D.I.: Ensemble based traffic light control for city zones using a reduced number of sensors. Transp. Res. Part C: Emerg. Technol. 46, 261–273 (2014)CrossRef

14.

Lim, P., Goh, C., Tan, K.: Evolutionary cluster-based synthetic oversampling ensemble (ECO-Ensemble) for imbalance learning. IEEE Trans. Cybern. 47, 2850–2861 (2017)CrossRef

15.

Kotsiantis, S.B.: Bagging and boosting variants for handling classifications problems: a survey. Knowl. Eng. Rev. 29, 78–100 (2014)CrossRef

16.

Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)MATH

17.

Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm, vol. 96, pp. 148–156 (1996)

18.

Del Jesus, M., Hoffmann, F., Navascués, L., Sánchez, L.: Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. Fuzzy Syst. 12, 296–308 (2004)CrossRef

19.

Lango, M., Stefanowski, J.: Multi-class and feature selection extensions of roughly balanced bagging for imbalanced data. J. Intell. Inf. Syst. 50(1), 97–127 (2018)CrossRef

20.

Jurek, A., Bi, Y., Wu, S., Nugent, C.: A survey of commonly used ensemble-based classification techniques. Knowl. Eng. Rev. 29(5), 551–581 (2013)CrossRef

21.

Mokeddem, D., Belbachir, H.: A survey of distributed classification based ensemble data mining methods. J. Appl. Sci. 9, 3739–3745 (2009)CrossRef

22.

Wang, S., Yao, X.: Multiclass imbalance problems: analysis and potential solutions. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42, 1119–1130 (2012)CrossRef

23.

Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37, 77–85 (1994)CrossRef

24.

Antonelli, M., Ducange, P., Marcelloni, F.: An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets. Neurocomputing 146, 125–136 (2014)CrossRef

25.

Harandi, F., Derhami, V.: A reinforcement learning algorithm for adjusting antecedent parameters and weights of fuzzy rules in a fuzzy classifier. J. Intell. Fuzzy Syst. 30, 2339–2347 (2016)CrossRef

26.

Kotsiantis, S., Kanellopoulos, D., Pintelas, P., et al.: Handling imbalanced datasets: a review. GESTS Int. Trans. Comput. Sci. Eng. 30, 25–36 (2006)

27.

Ramyachitra, D., Manikandan, P.: Imbalanced dataset classification and solutions: a review. Int. J. Comput. Bus. Res. (IJCBR) 5 (2014)

28.

Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59119-2_166CrossRef

29.

Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42, 463–484 (2012)CrossRef

30.

Hu, S., Liang, Y., Ma, L., He, Y.: MSMOTE: improving classification performance when training data is imbalanced. In: Second International Workshop on Computer Science and Engineering, WCSE 2009, vol. 2, pp. 13–17. IEEE (2009)

31.

Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40, 185–197 (2010)CrossRef

32.

Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009, pp. 324–331. IEEE (2009)

33.

Chawla, N.V., Lazarevic, A., Hall, L.O., Bowyer, K.W.: SMOTEBoost: improving prediction of the minority class in boosting. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 107–119. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39804-2_12CrossRef

34.

Del Jesus, M.J., Hoffmann, F., Navascués, L.J., Sánchez, L.: Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. Fuzzy Syst. 12, 296–308 (2004)CrossRef

35.

Otero, J., Sánchez, L.: Induction of descriptive fuzzy classifiers with the Logitboost algorithm. Soft Comput.- Fus. Found. Methodol. Appl. 10, 825–835 (2006)

36.

Alcala-Fdez, J., Alcala, R., Herrera, F.: A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans. Fuzzy Syst. 19, 857–872 (2011)CrossRef

37.

Quinlan, J.R.: C4.5: Programming for Machine Learning, vol. 38. Morgan Kaufmann, Burlington (1993)

38.

Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult.-Valued Log. Soft Comput. 17, 255–287 (2011)

39.

García, S., Herrera, F., Shawe-taylor, J.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)MATH

40.

Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6(2), 65–70 (1979)MathSciNetMATH

41.

Finner, H.: On a monotonicity problem in step-down multiple test procedures. J. Am. Stat. Assoc. 88, 920–923 (1993)MathSciNetCrossRef

Title: Ensemble and Fuzzy Techniques Applied to Imbalanced Traffic Congestion Datasets: A Comparative Study
Authors: Pedro Lopez-Garcia
Antonio D. Masegosa
Enrique Onieva
Eneko Osaba
Publisher: Springer International Publishing
Book: Bioinspired Optimization Methods and Their Applications
Print ISBN: 978-3-319-91640-8

Electronic ISBN: 978-3-319-91641-5

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91641-5_16

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