A survey on ensemble learning

Abstract

Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data. In this context, it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model. Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way. In this paper, we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics. In addition, we present challenges and possible research directions for each mainstream approach of ensemble learning, and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning, reinforcement learning, etc.

Keywords

ensemble learning / supervised ensemble classification / semi-supervised ensemble classification / clustering ensemble / semi-supervised clustering ensemble

References

[1] Zhou Z H. Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, 2012

[2] Dasarathy B V, Sheela B V. A composite classifier system design: concepts and methodology. Proceedings of the IEEE, 1979, 67(5): 708–713

[3] Kearns M. Learning boolean formulae or finite automata is as hard as factoring. Technical Report TR-14-88 Harvard University Aikem Computation Laboratory, 1988

[4] Schapire, Robert E. The strength of weak learnability. Machine Learning, 1990, 5(2): 197–227

[5] Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123–140

[6] Hastie T, Rosset S, Zhu J, Zou H. Multi-class adaboost. Statistics and its Interface, 2009, 2(3): 349–360

[7] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32

[8] Ho T K. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995, 278–282

[9] Friedman J H. Stochastic gradient boosting. Computational Statistics and Data Analysis, 2002, 38(4): 367–378

[10] Garcia-Pedrajas N. Constructing ensembles of classifiers by means of weighted instance selection. IEEE Transactions on Neural Networks, 2009, 20(2): 258–277

[11] Garcia-Pedrajas N, Maudes-Raedo J, Garcia-Osorio C, Rodriguez-Díez J J, Linden D E, Johnston S J. Supervised subspace projections for constructing ensembles of classifiers. Information Sciences, 2012, 193(11): 1–21

[12] Kuncheva L I, Rodriguez J J, Plumpton C O, Linden D E, Johnston S J. Random subspace ensembles for FMRI classification. IEEE Transactions on Medical Imaging, 2010, 29(2): 531–542

[13] Ye Y, Wu Q, Huang J Z, Ng M K, Li X. Stratified sampling for feature subspace selection in random forests for high dimensional data. Pattern Recognition, 2013, 46(3): 769–787

[14] Bryll R, Gutierrez-Osuna R, Quek F. Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition, 2003, 36(6): 1291–1302

[15] Blum A, Mitchell T. Combining labeled and unlabeled data with cotraining. In: Proceedings of the 11th Annual Conference on Computational Learning Theory. 1998, 92–100

[16] Wang J, Luo S W, Zeng X H. A random subspace method for cotraining. In: Proceedings of 2008 IEEE International Joint Conference on Neural Networks. 2008, 195–200

[17] Yaslan Y, Cataltepe Z. Co-training with relevant random subspaces. Neurocomputing, 2010, 73(10–12): 1652–1661

[18] Zhang J, Zhang D. A novel ensemble construction method for multiview data using random cross-view correlation between within-class examples. Pattern Recognition, 2011, 44(6): 1162–1171

[19] Guo Y, Jiao L, Wang S, Liu F, Rong K, Xiong T. A novel dynamic rough subspace based selective ensemble. Pattern Recognition, 2015, 48(5): 1638–1652

[20] Windeatt T, Duangsoithong R, Smith R. Embedded feature ranking for ensemble MLP classifiers. IEEE Transactions on Neural Networks, 2011, 22(6): 988–994

[21] Rodriguez J J, Kuncheva L I, Alonso C J. Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10): 1619–1630

[22] Takemura A, Shimizu A, Hamamoto K. Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection. IEEE Transactions on Medical Imaging, 2010, 29(3): 598–609

[23] Amasyali M F, Ersoy O K. Classifier ensembles with the extended space forest. IEEE Transactions on Knowledge and Data Engineering, 2013, 26(3): 549–562

[24] Polikar R, Depasquale J, Mohammed H S, Brown G, Kuncheva L I. Learn++.MF: a random subspace approach for the missing feature problem. Pattern Recognition, 2010, 43(11): 3817–3832

[25] Nanni L, Lumini A. Evolved feature weighting for random subspace classifier. IEEE Transactions on Neural Networks, 2008, 19(2): 363–366

[26] Kennedy J, Eberhart R C. A discrete binary version of the particle swarm optimization algorithm. Computational Cybernatics and Simulation, 1997, 5(1): 4104–4108

[27] Zhou Z H, Tang W. Selective ensemble of decision trees. In: Proceedings of International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. 2003, 476–483

[28] Diao R, Chao F, Peng T, Snooke N, Shen Q. Feature selection inspired classifier ensemble reduction. IEEE Transactions on Cybernetics, 2014, 44(8): 1259–1268

[29] Yu Z, Wang D, You J, Wong H S, Wu S, Zhang J, Han G. Progressive subspace ensemble learning. Pattern Recognition, 2016, 60: 692–705

[30] Yu Z, Wang D, Zhao Z, Chen C P, You J, Wong H S, Zhang J. Hybrid incremental ensemble learning for noisy real-world data classification. IEEE Transactions on Cybernetics, 2017, 99: 1–14

[31] Dos Santos E M, Sabourin R, Maupin P. A dynamic overproduceand- choose strategy for the selection of classifier ensembles. Pattern Recognition, 2008, 41(10): 2993–3009

[32] Hernández-Lobato D, Martínez-Muñoz G, Suárez A. Statistical instance-based pruning in ensembles of independent classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 364–369

[33] Martínez-Muñoz G, Hernández-Lobato D, Suárez A. An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 245–259

[34] De Stefano C, Folino G, Fontanella F, Di Freca A S. Using bayesian networks for selecting classifiers in GP ensembles. Information Sciences, 2014, 258: 200–216

[35] Rahman A, Verma B. Novel layered clustering-based approach for generating ensemble of classifierntis. IEEE Transactions on Neural Networks, 2011, 22(5): 781–792

[36] Verma B, Rahman A. Cluster-oriented ensemble classifier: impact of multicluster characterization on ensemble classifier learning. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(4): 605–618

[37] Zhang L, Suganthan P N. Oblique decision tree ensemble via multisurface proximal support vector machine. IEEE Transactions on Cybernetics, 2015, 45(10): 2165–2176

[38] Tan P J, Dowe D L. Decision forests with oblique decision trees. In: Proceedings of Mexican International Conference on Artificial Intelligence. 2006, 593–603

[39] Zhou Z H, Wu J, Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence, 2002, 137(1–2): 239–263

[40] Yu Z, Chen H, Liu J, You J, Leung H, Han G. Hybrid k-nearest neighbor classifier. IEEE Transactions on Cybernetics, 2016, 46(6): 1263–1275

[41] Li H, Wen G, Yu Z, Zhou T. Random subspace evidence classifier. Neurocomputing, 2013, 110(13): 62–69

[42] Hernández-Lobato D, Martínez-Muñoz G, Suárez A. How large should ensembles of classifiers be? Pattern Recognition, 2013, 46(5): 1323–1336

[43] Wang X Z, Xing H J, Li Y, Hua Q, Dong C R, Pedrycz W. A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Transactions on Fuzzy Systems, 2015, 23(5): 1638–1654

[44] Kuncheva L I. A bound on kappa-error diagrams for analysis of classifier ensembles. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 494–501

[45] Gao W, Zhou Z H. Approximation stability and boosting. In: Proceedings of International Conference on Algorithmic Learning Theory. 2010, 59–73

[46] Yin X C, Huang K, Hao H W, Iqbal K, Wang Z B. A novel classifier ensemble method with sparsity and diversity. Neurocomputing, 2014, 134: 214–221

[47] Zhang L, Suganthan P N. Random forests with ensemble of feature spaces. Pattern Recognition, 2014, 47(10): 3429–3437

[48] Li N, Yu Y, Zhou Z H. Diversity regularized ensemble pruning. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2012, 330–345

[49] Zhang D, Chen S, Zhou Z H, Yang Q. Constraint projections for ensemble learning. In: Proceedings of the 23rd National Conference on Artifical Intelligence-Volume 2. 2008, 758–763

[50] Zhou Z H, Li N. Multi-information ensemble diversity. In: Proceedings of International Workshop on Multiple Classifier Systems. 2010, 134–144

[51] Sun T, Zhou Z H. Structural diversity for decision tree ensemble learning. Frontiers of Computer Science, 2018, 12(3): 560–570

[52] Mao S, Jiao L, Xiong L, Gou S, Chen B, Yeung S K. Weighted classifier ensemble based on quadratic form. Pattern Recognition, 2015, 48(5): 1688–1706

[53] Yu Z, Wang Z, You J, Zhang J, Liu J, Wong H S, Han G. A new kind of nonparametric test for statistical comparison of multiple classifiers over multiple datasets. IEEE Transactions on Cybernetics, 2017, 47(12): 4418–4431

[54] Kim K J, Cho S B. An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Transactions on Evolutionary Computation, 2008, 12(3): 377–388

[55] Qian C, Yu Y, Zhou Z H. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015

[56] Zhou Z H, Feng J. Deep forest: towards an alternative to deep neural networks. 2017, arXiv preprint arXiv:1702.08835

[57] Feng J, Zhou Z H. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018

[58] Zhang Y L, Zhou J, Zheng W, Feng J, Li L, Liu Z, Zhou Z H. Distributed deep forest and its application to automatic detection of cashout fraud. 2018, arXiv preprint arXiv:1805.04234

[59] Feng J, Yu Y, Zhou Z H. Multi-layered gradient boosting decision trees. In: Proceedings of Advances in Neural Information Processing Systems. 2018, 3555–3565

[60] Pang M, Ting K M, Zhao P, Zhou Z H. Improving deep forest by confidence screening. In: Proceedings of the 18th IEEE International Conference on Data Mining. 2018, 1194–1199

[61] Yu Z, Li L, Liu J, Han G. Hybrid adaptive classifier ensemble. IEEE Transactions on Cybernetics, 2015, 45(2): 177–190

[62] Zhou Z H, Zhang M L. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155–170

[63] Zhu X, Zhang P, Lin X, Shi Y. Active learning from stream data using optimal weight classifier ensemble. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(6): 1607–1621

[64] Brzezinski D, Stefanowski J. Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(1): 81–94

[65] Muhlbaier M D, Topalis A, Polikar R. Learn++.NC: combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE Transactions on Neural Networks, 2009, 20(1): 152–168

[66] Xiao J, He C, Jiang X, Liu D. A dynamic classifier ensemble selection approach for noise data. Information Sciences, 2010, 180(18): 3402–3421

[67] 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 Transactions on Systems Man and Cybernetics Part C, 2012, 42(4): 463–484

[68] Liu X Y, Wu J, Zhou Z H. Exploratory under-sampling for classimbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(2): 539–550

[69] Sun B, Chen H, Wang J, Xie H. Evolutionary under-sampling based bagging ensemble method for imbalanced data classification. Frontiers of Computer Science, 2018, 12(2): 331–350

[70] Li Q, Li G, Niu W, Cao Y, Chang L, Tan J, Guo L. Boosting imbalanced data learning with wiener process oversampling. Frontiers of Computer Science, 2017, 11(5): 836–851

[71] Abawajy J H, Kelarev A, Chowdhury M. Large iterative multitier ensemble classifiers for security of big data. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3): 352–363

[72] Li N, Zhou Z H. Selective ensemble of classifier chains. In: Proceedings of International Workshop on Multiple Classifier Systems. 2013, 146–156

[73] Li N, Jiang Y, Zhou Z H. Multi-label selective ensemble. In: Proceedings of International Workshop on Multiple Classifier Systems. 2015, 76–88

[74] Yu Z, Deng Z, Wong H S, Tan L. Identifying protein-kinase-specific phosphorylation sites based on the Bagging-AdaBoost ensemble approach. IEEE Transactions on Nanobioscience, 2010, 9(2): 132–143

[75] Yu D J, Hu J, Yang J, Shen H B, Tang J, Yang J Y. Designing templatefree predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering. IEEE/ACMTransactions on Computational Biology and Bioinformatics, 2013, 10(4): 994–1008

[76] Yu G, Rangwala H, Domeniconi C, Zhang G, Yu Z. Protein function prediction using multilabel ensemble classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013, 10(4): 1

[77] Daliri M R. Combining extreme learning machines using support vector machines for breast tissue classification. Computer Methods in Biomechanics and Biomedical Engineering, 2015, 18(2): 185–191

[78] Oliveira L, Nunes U, Peixoto P. On exploration of classifier ensemble synergism in pedestrian detection. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1): 16–27

[79] Xu Y, Cao X, Qiao H. An efficient tree classifier ensemble-based approach for pedestrian detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41(1): 107–117

[80] Zhang B. Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 322–332

[81] Sun S, Zhang C. The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2): 367–373

[82] Su Y, Shan S, Chen X, Gao W. Hierarchical ensemble of global and local classifiers for face recognition. IEEE Transactions on Image Processing, 2009, 18(8): 1885–1896

[83] Zhang P, Bui T D, Suen C Y. A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition, 2007, 40(12): 3415–3429

[84] Xu X S, Xue X, Zhou Z H. Ensemble multi-instance multi-label learning approach for video annotation task. In: Proceedings of the 19th ACM International Conference on Multimedia. 2011, 1153–1156

[85] Hautamaki V, Kinnunen T, Sedlák F, Lee K A, Ma B, Li H. Sparse classifier fusion for speaker verification. IEEE Transactions on Audio Speech and Language Processing, 2013, 21(8): 1622–1631

[86] Guan Y, Li C T, Roli F. On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(7): 1521–1528

[87] Tao D, Tang X, Li X, Wu X. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(7): 1088–1099

[88] Hu W, Hu W, Maybank S. AdaBoost-based algorithm for network intrusion detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008, 38(2): 577–583

[89] Zhang P, Zhu X, Shi Y, Guo L, Wu X. Robust ensemble learning for mining noisy data streams. Decision Support Systems, 2011, 50(2): 469–479

[90] Yu L, Wang S, Lai K K. Developing an SVM-based ensemble learning system for customer risk identification collaborating with customer relationship management. Frontiers of Computer Science, 2010, 4(2): 196–203

[91] Fersini E, Messina E, Pozzi F A. Sentiment analysis: Bayesian ensemble learning. Decision Support Systems, 2014, 68: 26–38

[92] Yu G, Zhang G, Yu Z, Domeniconi C, You J, Han G. Semi-supervised ensemble classification in subspaces. Applied Soft Computing, 2012, 12(5): 1511–1522

[93] Yu Z, Zhang Y, Chen C L P, You J, Wong H S, Dai D, Wu S, Zhang J. Multiobjective semisupervised classifier ensemble. IEEE Transactions on Cybernetics, 2019, 49(6): 2280–2293

[94] Gharroudi O, Elghazel H, Aussem A. A semi-supervised ensemble approach for multi-label learning. In: Proceedings of the 16th IEEE International Conference on Data Mining Workshops (ICDMW). 2016, 1197–1204

[95] Lu X, Zhang J, Li T, Zhang Y. Hyperspectral image classification based on semi-supervised rotation forest. Remote Sensing, 2017, 9(9): 924

[96] Wang S, Chen K. Ensemble learning with active data selection for semi-supervised pattern classification. In: Proceedings of 2007 International Joint Conference on Neural Networks. 2007, 355–360

[97] Soares R G F, Chen H, Yao X. A cluster-based semi-supervised ensemble for multiclass classification. IEEE Transactions on Emerging Topics in Computational Intelligence, 2017, 1(6): 408–420

[98] Woo H, Park C H. Semi-supervised ensemble learning using label propagation. In: Proceedings of the 12th IEEE International Conference on Computer and Information Technology. 2012, 421–426

[99] Zhang M L, Zhou Z H. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98–129

[100] Alves M, Bazzan A L C, Recamonde-Mendoza M. Social-training: ensemble learning with voting aggregation for semi-supervised classification tasks. In: Proceedings of 2017 Brazilian Conference on Intelligent Systems (BRACIS). 2017, 7–12

[101] Yu Z, Lu Y, Zhang J, You J, Wong H S, Wang Y, Han G. Progressive semi-supervised learning of multiple classifiers. IEEE Transactions on Cybernetics, 2018, 48(2): 689–702

[102] Hosseini M J, Gholipour A, Beigy H. An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams. Knowledge and Information Systems, 2016, 46(3): 567–597

[103] Wang Y, Li T. Improving semi-supervised co-forest algorithm in evolving data streams. Applied Intelligence, 2018, 48(10): 3248–3262

[104] Yu Z, Zhang Y, You J, Chen C P, Wong H S, Han G, Zhang J. Adaptive semi-supervised classifier ensemble for high dimensional data classification. IEEE Transactions on Cybernetics, 2019, 49(2): 366–379

[105] Li M, Zhou Z H. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man, and Cybernetics- Part A: Systems and Humans, 2007, 37(6): 1088–1098

[106] Guz U, Cuendet S, Hakkani-Tur D, Tur G. Multi-view semisupervised learning for dialog act segmentation of speech. IEEE Transactions on Audio Speech and Language Processing, 2010, 18(2): 320–329

[107] Shi L, Ma X, Xi L, Duan Q, Zhao J. Rough set and ensemble learning based semi-supervised algorithm for text classification. Expert Systems with Applications, 2011, 38(5): 6300–6306

[108] Abdelgayed T S, Morsi W G, Sidhu T S. Fault detection and classification based on co-training of semi-supervised machine learning. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1595–1605

[109] Saydali S, Parvin H, Safaei A A. Classifier ensemble by semisupervised learning: local aggregation methodology. In: Proceedings of International Doctoral Workshop on Mathematical and Engineering Methods in Computer Science. 2015, 119–132

[110] Shao W, Tian X. Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development. Neurocomputing, 2017, 222: 91–104

[111] Ahmed I, Ali R, Guan D, Lee Y K, Lee S, Chung T. Semi-supervised learning using frequent itemset and ensemble learning for SMS classification. Expert Systems with Applications, 2015, 42(3): 1065–1073

[112] Strehl A, Ghosh J. Cluster ensembles: a knowledge reuse framework for combining partitionings. Journal of Machine Learning Research, 2002, 3(3): 583–617

[113] Yang F, Li X, Li Q, Li T. Exploring the diversity in cluster ensemble generation: random sampling and random projection. Expert Systems with Applications, 2014, 41(10): 4844–4866

[114] Wu O, Hu W, Maybank S J, Zhu M, Li B. Efficient clustering aggregation based on data fragments. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(3): 913–926

[115] Franek L, Jiang X. Ensemble clustering by means of clustering embedding in vector spaces. Pattern Recognition, 2014, 47(2): 833–842

[116] Yu Z, Wong H S, Wang H. Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics, 2007, 23(21): 2888–2896

[117] Yu Z, Wong H S, You J, Yu G, Han G. Hybrid cluster ensemble framework based on the random combination of data transformation operators. Pattern Recognition, 2012, 45(5): 1826–1837

[118] Yu Z, Li L, You J, Wong H S, Han G. SC3: triple spectral clusteringbased consensus clustering framework for class discovery from cancer gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012, 9(6): 1751–1765

[119] Yu Z, Chen H, You J, Han G, Li L. Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013, 10(3): 657–670

[120] Yu Z, Li L, Liu J, Zhang J, Han G. Adaptive noise immune cluster ensemble using affinity propagation. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(12): 3176–3189

[121] Ayad H G, Kamel M S. On voting-based consensus of cluster ensembles. Pattern Recognition, 2010, 43(5): 1943–1953

[122] Zhang S, Wong H S, Shen Y. Generalized adjusted rand indices for cluster ensembles. Pattern Recognition, 2012, 45(6): 2214–2226

[123] Fred A L N, Jain A K. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 835–850

[124] Lourenco A, Fred A L N, Jain A K. On the scalability of evidence accumulation clustering. In: Proceedings of the 20th International Conference on Pattern Recognition. 2010, 782–785

[125] Amasyali M F, Ersoy O. The performance factors of clustering ensembles. In: Proceedings of the 16th IEEE Signal Processing, Communication and Applications Conference. 2008, 1–4

[126] Fern X Z, Brodley C E. Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03). 2003, 186–193

[127] Kuncheva L I, Whitaker C J. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 2003, 51(2): 181–207

[128] Kuncheva L I, Vetrov D P. Evaluation of stability of k-means cluster ensembles with respect to random initialization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(11): 1798–1808

[129] Shi Y, Yu Z, Chen C L P, You J, Wong H S, Wang Y D, Zhang J. Transfer clustering ensemble selection. IEEE Transactions on Cybernetics, 2018, PP(99): 1–14

[130] Topchy A P, Law M H C, Jain A K, Fred A L. Analysis of consensus partition in cluster ensemble. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04). 2004, 225–232

[131] Wang T. CA-tree: a hierarchical structure for efficient and scalable coassociation-based cluster ensembles. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 41(3): 686–698

[132] Hore P, Hall L O, Goldgof D B. A scalable framework for cluster ensembles. Pattern Recognition, 2009, 42(5): 676–688

[133] Fern X Z, Lin W. Cluster ensemble selection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2008, 1(3): 128–141

[134] Azimi J, Fern X. Adaptive cluster ensemble selection. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009, 992–997

[135] Wang X, Han D, Han C. Rough set based cluster ensemble selection. In: Proceedings of the 16th International Conference on Information Fusion. 2013, 438–444

[136] Yu Z, Li L, Gao Y, You J, Liu J, Wong H S, Han G. Hybrid clustering solution selection strategy. Pattern Recognition, 2014, 47(10): 3362–3375

[137] Yu Z, You J, Wong H S, Han G. From cluster ensemble to structure ensemble. Information Sciences, 2012, 198: 81–99

[138] Yu Z, Li L, Wong H S, You J, Han G, Gao Y, Yu G. Probabilistic cluster structure ensemble. Information Sciences, 2014, 267(5): 16–34

[139] Yu Z, Zhu X, Wong H S, You J, Zhang J, Han G. Distribution-based cluster structure selection. IEEE Transactions on Cybernetics, 2017, 47(11): 3554–3567

[140] Yang Y, Jiang J. HMM-based hybrid meta-clustering ensemble for temporal data. Knowledge-Based Systems, 2014, 56: 299–310

[141] Yang Y, Chen K. Temporal data clustering via weighted clustering ensemble with different representations. IEEE Transactions on Knowledge and Data Engineering, 2010, 23(2): 307–320

[142] Yu Z, Wong H S. Class discovery from gene expression data based on perturbation and cluster ensemble. IEEE Transactions on Nanobioscience, 2009, 8(2): 147–160

[143] Yu Z, Chen H, You J, Liu J, Wong H S, Han G, Li L. Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015, 12(4): 887–901

[144] Avogadri R, Valentini G. Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artificial Intelligence in Medicine, 2009, 45(2): 173–183

[145] Mimaroglu S, Aksehirli E. DICLENS: divisive clustering ensemble with automatic cluster number. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012, 9(2): 408–420

[146] Alush A, Goldberger J. Ensemble segmentation using efficient integer linear programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1966–1977

[147] Li H, Meng F, Wu Q, Luo B. Unsupervised multiclass region cosegmentation via ensemble clustering and energy minimization. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(5): 789–801

[148] Zhang X, Jiao L, Liu F, Bo L, Gong M. Spectral clustering ensemble applied to SAR image segmentation. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(7): 2126–2136

[149] Jia J, Liu B, Jiao L. Soft spectral clustering ensemble applied to image segmentation. Frontiers of Computer Science, 2011, 5(1): 66–78

[150] Rafiee G, Dlay S S, Woo W L. Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches. Pattern Recognition, 2013, 46(10): 2685–2699

[151] Huang X, Zheng X, Yuan W, Wang F, Zhu S. Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization. Information Sciences, 2011, 181(11): 2293–2302

[152] Bassiou N, Moschou V, Kotropoulos C. Speaker diarization exploiting the eigengap criterion and cluster ensembles. IEEE Transactions on Audio Speech and Language Processing, 2010, 18(8): 2134–2144

[153] Zhuang W, Ye Y, Chen Y, Li T. Ensemble clustering for internet security applications. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(6): 1784–1796

[154] Tsai C F, Hung C. Cluster ensembles in collaborative filtering recommendation. Applied Soft Computing, 2012, 12(4): 1417–1425

[155] Yu Z, Luo P, You J, Wong H S, Leung H, Wu S, Zhang J, Han G. Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(3): 701–714

[156] Yu Z, Kuang Z, Liu J, Chen H, Zhang J, You J, Wong H S, Han G. Adaptive ensembling of semi-supervised clustering solutions. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1577–1590

[157] Wei S, Li Z, Zhang C. Combined constraint-based with metric-based in semi-supervised clustering ensemble. International Journal of Machine Learning and Cybernetics, 2018, 9(7): 1085–1100

[158] Karypis G, Han E H S, Kumar V. Chameleon: hierarchical clustering using dynamic modeling. Computer, 1999, 32(8): 68–75

[159] Xiao W, Yang Y, Wang H, Li T, Xing H. Semi-supervised hierarchical clustering ensemble and its application. Neurocomputing, 2016, 173: 1362–1376

[160] Zhou Z H, Tang W. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77–83

[161] Zhang J, Yang Y, Wang H, Mahmood A, Huang F. Semi-supervised clustering ensemble based on collaborative training. In: Proceedings of International Conference on Rough Sets and Knowledge Technology. 2012, 450–455

[162] Zhou Z H, Li M. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(11): 1529–1541

[163] Wang H, Yang D, Qi J. Semi-supervised cluster ensemble based on normal mutual information. Energy Procedia, 2011, 13: 1673–1677

[164] Yu Z, Luo P, Liu J, Wong H S, You J, Han G, Zhang J. Semisupervised ensemble clustering based on selected constraint projection. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2394–2407

[165] Yang Y, Teng F, Li T, Wang H, Zhang Q. Parallel semi-supervised multi-ant colonies clustering ensemble based on mapreduce methodology. IEEE Transactions on Cloud Computing, 2018, 6(3): 857–867

[166] Iqbal A M, Moh’D A, Khan Z. Semi-supervised clustering ensemble by voting. Computer Science, 2012, 2(9): 33–40

[167] Chen D, Yang Y, Wang H, Mahmood A. Convergence analysis of semi-supervised clustering ensemble. In: Proceedings of International Conference on Information Science and Technology. 2014, 783–788

[168] Yan B, Domeniconi C. Subspace metric ensembles for semisupervised clustering of high dimensional data. In: Proceedings of European Conference on Machine Learning. 2006, 509–520

[169] Mahmood A, Li T, Yang Y, Wang H, Afzal M. Semi-supervised clustering ensemble for Web video categorization. In: Proceedings of International Workshop on Multiple Classifier Systems. 2013, 190–200

[170] Mahmood A, Li T, Yang Y, Wang H, Afzal M. Semi-supervised evolutionary ensembles for web video categorization. Knowledge-Based Systems, 2015, 76: 53–66

[171] Junaidi A, Fink G A. A semi-supervised ensemble learning approach for character labeling with minimal human effort. In: Proceedings of 2011 International Conference on Document Analysis and Recognition. 2011, 259–263

[172] Yu Z, Wongb H S, You J, Yang Q, Liao H. Knowledge based cluster ensemble for cancer discovery from biomolecular data. IEEE Transactions on Nanobioscience, 2011, 10(2): 76–85

[173] Yu Z, Chen H, You J, Wong H S, Liu J, Li L, Han G. Double selection based semi-supervised clustering ensemble for tumor clustering from gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2014, 11(4): 727–740

[174] Krogh A, Vedelsby J. Neural network ensembles, cross validation and active learning. In: Proceedings of the 7th International Conference on Neural Information Processing Systems. 1994, 231–238

[175] Yin Z, Zhao M, Wang Y, Yang J, Zhang J. Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. Computer Methods and Programs in Biomedicine, 2017, 140: 93–110

[176] Kumar A, Kim J, Lyndon D, Fulham M, Feng D. An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE Journal of Biomedical and Health Informatics, 2017, 21(1): 31–40

[177] Liu W, Zhang M, Luo Z, Ca i Y. An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access, 2017, 5: 24417–24425

[178] Kandaswamy C, Silva L M, Alexandre L A, Santos J M. Deep transfer learning ensemble for classification. In: Proceedings of International Work-Conference on Artificial Neural Networks. 2015, 335–348

[179] Nozza D, Fersini E, Messina E. Deep learning and ensemble methods for domain adaptation. In: Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). 2016, 184–189

[180] Liu X, Liu Z, Wang G, Cai Z, Zhang H. Ensemble transfer learning algorithm. IEEE Access, 2018, 6: 2389–2396

[181] Brys T, Harutyunyan A, Vrancx P, Nowé A, Taylor M E. Multiobjectivization and ensembles of shapings in reinforcement learning. Neurocomputing, 2017, 263: 48–59

[182] Chen X L, Cao L, Li C X, Xu Z X, Lai J. Ensemble network architecture for deep reinforcement learning. Mathematical Problems in Engineering, 2018, 2018: 1–6

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