Dynamic ensemble selection based on hesitant fuzzy multiple criteria decision making. (English) Zbl 1491.68235
Summary: Among several extensions of fuzzy sets, hesitant fuzzy sets (HFSs) are interesting and practical. This paper proposes an application of HFSs in multiple classifier systems (MCSs). The MCSs have been proven as an effective and robust strategy for classification problems. These systems combine different classifiers and generally are composed of three steps: generation, selection (optional) and integration. This paper focuses on the selection step and proposes a novel dynamic ensemble selection method. In particular, the proposed method employs some selection criteria to determine the range of competency of the classifiers, and then, a HMCDM (hesitant fuzzy multiple criteria decision making) method is utilized to select the appropriate classifiers. Experimental results show that the proposed framework improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques. The Quade nonparametric statistical test confirms the capability of our proposed method.
MSC:
| 68T37 | Reasoning under uncertainty in the context of artificial intelligence |
Keywords:
multiple-classifier systems; dynamic ensemble selection; competence measure; hesitant fuzzy sets; multiple-criteria decision makingReferences:
| [1] | Alcalá-Fdez, J.; S��nchez, L.; García, S.; del Jesus, MJ; Ventura, S.; Garrell, JM; Otero, J.; Romero, C.; Bacardit, J.; Rivas, VM; Fernández, JC; Herrera, F., KEEL: a software tool to assess evolutionary algorithms to data mining problems, Soft Comput, 13, 3, 307-318 (2009) · doi:10.1007/s00500-008-0323-y |
| [2] | Aliahmadipour, L.; Torra, V.; Eslami, E.; Eftekhari, M., A definition for hesitant fuzzy partitions, Int J Comput Intell Syst, 9, 497-505 (2016) · doi:10.1080/18756891.2016.1175814 |
| [3] | Bashbaghi, S.; Granger, E.; Sabourin, R.; Bilodeau, GA, Dynamic ensembles of exemplar-svms for still-to-video face recognition, Pattern Recognit, 69, C, 61-81 (2017) · doi:10.1016/j.patcog.2017.04.014 |
| [4] | Breiman, L., Bagging predictors, Mach Learn, 24, 2, 123-140 (1996) · Zbl 0858.68080 · doi:10.1007/BF00058655 |
| [5] | Breiman, L., Random forests, Mach Learn, 45, 1, 5-32 (2001) · Zbl 1007.68152 · doi:10.1023/A:1010933404324 |
| [6] | Britto, AS Jr; Sabourin, R.; Soares de Oliveira, L., Dynamic selection of classifiers—a comprehensive review, Pattern Recognit, 47, 3665-3680 (2014) · doi:10.1016/j.patcog.2014.05.003 |
| [7] | Cavalin, PR; Sabourin, R.; Suen, CY, Logid: an adaptive framework combining local and global incremental learning for dynamic selection of ensembles of hmms, Pattern Recognit, 45, 9, 3544-3556 (2012) · doi:10.1016/j.patcog.2012.02.034 |
| [8] | Cavalin, PR; Sabourin, R.; Suen, CY, Dynamic selection approaches for multiple classifier systems, Neural Comput. Appl., 22, 3, 673-688 (2013) · doi:10.1007/s00521-011-0737-9 |
| [9] | Cruz RMO, Oliveira DVR, Cavalcanti GDC, Sabourin R (2018b) FIRE-DES++: enhanced online pruning of base classifiers for dynamic ensemble selection. CoRR arXiv:1810.00520 |
| [10] | Cruz RMO, Sabourin R, Cavalcanti GDC, Ren TI (2018c) META-DES: a dynamic ensemble selection framework using meta-learning. CoRR arXiv:1810.01270 |
| [11] | Cruz, RM; Sabourin, R.; Cavalcanti, GD, Dynamic classifier selection, Inf Fusion, 41, C, 195-216 (2018) · doi:10.1016/j.inffus.2017.09.010 |
| [12] | de Almeida PRL, da Silva Júnior EJ, Celinski TM, de Souza Britto A, de Oliveira LES, Koerich AL (2012) Music genre classification using dynamic selection of ensemble of classifiers. In: 2012 IEEE international conference on systems, man, and cybernetics (SMC), pp 2700-2705. 10.1109/ICSMC.2012.6378155 |
| [13] | Didaci, L.; Giacinto, G.; Roli, F.; Marcialis, G., Rapid and brief communication: a study on the performances of dynamic classifier selection based on local accuracy estimation, Pattern Recognit, 38, 2188-2191 (2005) · Zbl 1077.68797 · doi:10.1016/j.patcog.2005.02.010 |
| [14] | Duda, RO; Hart, PE; Stork, DG, Pattern Classif (2000), New York: Wiley, New York |
| [15] | Ebrahimpour, M.; Eftekhari, M., Ensemble of feature selection methods: a hesitant fuzzy sets approach, Appl Soft Comput (2016) · doi:10.1016/j.asoc.2016.11.021 |
| [16] | Ebrahimpour, M.; Eftekhari, M., Distributed feature selection: a hesitant fuzzy correlation concept for microarray high-dimensional datasets, Chemom Intell Lab Syst, 173, 51 (2018) · doi:10.1016/j.chemolab.2018.01.001 |
| [17] | Farhadinia, B., A series of score functions for hesitant fuzzy sets, Inf Sci, 277, 102-110 (2014) · Zbl 1354.03074 · doi:10.1016/j.ins.2014.02.009 |
| [18] | Feng, J.; Wang, L.; Sugiyama, M.; Yang, C.; Zhou, ZH; Zhang, C., Boosting and margin theory, Front Electr Electron Eng, 7, 127 (2012) · doi:10.1007/s11460-012-0188-9 |
| [19] | Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the second European conference on computational learning theory, Springer, London, EuroCOLT ’95, pp 23-37, http://dl.acm.org/citation.cfm?id=646943.712093 |
| [20] | 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 (Applications and Reviews), 42, 4, 463-484 (2012) · doi:10.1109/TSMCC.2011.2161285 |
| [21] | Giacinto, G.; Roli, F., Dynamic classifier selection based on multiple classifier behaviour, Pattern Recognit, 34, 1879-1881 (2001) · Zbl 0995.68100 · doi:10.1016/S0031-3203(00)00150-3 |
| [22] | Giacinto, G.; Roli, F.; Didaci, L., Fusion of multiple classifiers for intrusion detection in computer networks, Pattern Recognit Lett, 24, 12, 1795-1803 (2003) · doi:10.1016/S0167-8655(03)00004-7 |
| [23] | Giacinto, G.; Perdisci, R.; Del Rio, M.; Roli, F., Intrusion detection in computer networks by a modular ensemble of one-class classifiers, Inf Fusion, 9, 1, 69-82 (2008) · doi:10.1016/j.inffus.2006.10.002 |
| [24] | Hongshan Xiao, ZX; Wang, Y., Ensemble classification based on supervised clustering for credit scoring, Appl Soft Comput, 43, 73-86 (2016) · doi:10.1016/j.asoc.2016.02.022 |
| [25] | Jahrer M, Töscher A, Legenstein R (2010) Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, KDD ’10, pp 693-702. 10.1145/1835804.1835893 |
| [26] | Jin, F.; Ni, Z.; Chen, H., Note on “hesitant fuzzy prioritized operators and their application to multiple attribute decision making”, Knowl Based Syst, 96, C, 115-119 (2016) · doi:10.1016/j.knosys.2015.12.023 |
| [27] | Ko, AHR; Sabourin, R.; Britto, AS Jr, From dynamic classifier selection to dynamic ensemble selection, Pattern Recognit, 41, 5, 1718-1731 (2008) · Zbl 1140.68466 · doi:10.1016/j.patcog.2007.10.015 |
| [28] | Krawczyk, B.; Minku, LL; Gama, J.; Stefanowski, J.; Woniak, M., Ensemble learning for data stream analysis, Inf Fusion, 37, C, 132-156 (2017) · doi:10.1016/j.inffus.2017.02.004 |
| [29] | Kuncheva, LI; Roli, F.; Kittler, J.; Windeatt, T., Classifier ensembles for changing environments, Multiple classifier systems, 1-15 (2004), Heidelberg: Springer, Heidelberg |
| [30] | Kurzynski M, Trajdos P (2017) On a new competence measure applied to the dynamic selection of classifiers ensemble. In: International conference on discovery science, pp 93-107 |
| [31] | Mohtashami, M.; Eftekhari, M., A hybrid filter-based feature selection method via hesitant fuzzy and rough sets concepts, Iran J Fuzzy Syst, 16, 2, 165-182 (2019) · Zbl 1429.68234 · doi:10.22111/ijfs.2019.4550 |
| [32] | Nucci, DD; Palomba, F.; Oliveto, R.; Lucia, AD, Dynamic selection of classifiers in bug prediction: an adaptive method, IEEE Trans Emerg Top Comput Intell, 1, 3, 202-212 (2017) · doi:10.1109/TETCI.2017.2699224 |
| [33] | Panichella A, Oliveto R, Lucia AD (2014) Cross-project defect prediction models: L’union fait la force. In: 2014 software evolution week—IEEE conference on software maintenance, reengineering, and reverse engineering (CSMR-WCRE), pp 164-173, 10.1109/CSMR-WCRE.2014.6747166 |
| [34] | Polikar, R.; Upda, L.; Upda, SS; Honavar, V., Learn++: an incremental learning algorithm for supervised neural networks, IEEE Trans Syst Man Cybern Part C (Applications and Reviews), 31, 4, 497-508 (2001) · doi:10.1109/5326.983933 |
| [35] | Porcel, C.; Tejeda-Lorente, A.; Martínez, MA; Herrera-Viedma, E., A hybrid recommender system for the selective dissemination of research resources in a technology transfer office, Inf Sci, 184, 1, 1-19 (2012) · Zbl 1238.68183 · doi:10.1016/j.ins.2011.08.026 |
| [36] | Rodriguez, JJ; Kuncheva, LI; Alonso, CJ, Rotation forest: a new classifier ensemble method, IEEE Trans Pattern Anal Mach Intell, 28, 10, 1619-1630 (2006) · doi:10.1109/TPAMI.2006.211 |
| [37] | Rodríguez, R.; Martinez, L.; Torra, V.; Xu, Z.; Herrera, F., Hesitant fuzzy sets: state of the art and future directions, Int J Intell Syst, 29, 495-524 (2014) · doi:10.1002/int.21654 |
| [38] | Sardari, S.; Eftekhari, M.; Afsari, F., Hesitant fuzzy decision tree approach for highly imbalanced data classification, Appl Soft Comput, 61, 727-741 (2017) · doi:10.1016/j.asoc.2017.08.052 |
| [39] | Skurichina, M.; Duin, RPW, Bagging for linear classifiers, Pattern Recognit, 31, 909-930 (1998) · doi:10.1016/S0031-3203(97)00110-6 |
| [40] | Stefan Lessmann, HVS; Baesens, B.; Thomas, LC, Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research, Eur J Oper Res, 247, 124-136 (2015) · Zbl 1346.90835 · doi:10.1016/j.ejor.2015.05.030 |
| [41] | Torra, V., Hesitant fuzzy sets, Int J Intell Syst, 25, 6, 529-539 (2010) · Zbl 1198.03076 · doi:10.1002/int.v25:6 |
| [42] | Wang, B.; Mao, Z., Outlier detection based on gaussian process with application to industrial processes, Appl Soft Comput (2018) · doi:10.1016/j.asoc.2018.12.029 |
| [43] | Werro, N., Fuzzy classification of online customers (2008), Berlin: Springer, Berlin |
| [44] | Woloszynski, T.; Kurzynski, M., A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognit, 44, 10-11, 2656-2668 (2011) · Zbl 1218.68155 · doi:10.1016/j.patcog.2011.03.020 |
| [45] | Woloszynski, T.; Kurzynski, M.; Podsiadlo, P.; Stachowiak, GW, A measure of competence based on random classification for dynamic ensemble selection, Inf Fusion INFFUS, 13, 207 (2012) · doi:10.1016/j.inffus.2011.03.007 |
| [46] | Wozniak, M.; Graña, M.; Corchado, E., A survey of multiple classifier systems as hybrid systems, Inf Fusion, 16, 3-17 (2014) · doi:10.1016/j.inffus.2013.04.006 |
| [47] | Xia, M.; Xu, Z., Hesitant fuzzy information aggregation in decision making, Int J Approx Reason, 52, 395-407 (2011) · Zbl 1217.68216 · doi:10.1016/j.ijar.2010.09.002 |
| [48] | Zhang, ZL; Chen, YY; Li, J.; Luo, XG, A distance-based weighting framework for boosting the performance of dynamic ensemble selection, Inf Process Manag, 56, 1300-1316 (2019) · doi:10.1016/j.ipm.2019.03.009 |
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