A survey on ensemble learning

Springer Science and Business Media LLC - Tập 14 - Trang 241-258 - 2019
Xibin Dong1, Zhiwen Yu1, Wenming Cao2, Yifan Shi1, Qianli Ma1
1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
2Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China

Tóm tắt

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.

Tài liệu tham khảo

Zhou Z H. Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, 2012 Dasarathy B V, Sheela B V. A composite classifier system design: concepts and methodology. Proceedings of the IEEE, 1979, 67(5): 708–713 Kearns M. Learning boolean formulae or finite automata is as hard as factoring. Technical Report TR-14-88 Harvard University Aikem Computation Laboratory, 1988 Schapire, Robert E. The strength of weak learnability. Machine Learning, 1990, 5(2): 197–227 Breiman L. Bagging predictors. Machine Learning, 1996, 24(2): 123–140 Hastie T, Rosset S, Zhu J, Zou H. Multi-class adaboost. Statistics and its Interface, 2009, 2(3): 349–360 Breiman L. Random forests. Machine Learning, 2001, 45(1): 5–32 Ho T K. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. 1995, 278–282 Friedman J H. Stochastic gradient boosting. Computational Statistics and Data Analysis, 2002, 38(4): 367–378 Garcia-Pedrajas N. Constructing ensembles of classifiers by means of weighted instance selection. IEEE Transactions on Neural Networks, 2009, 20(2): 258–277 Garcia-Pedrajas N, Maudes-Raedo J, Garcia-Osorio C, Rodriguez-Díez J J, Linden D E, Johnston SJ. Supervised subspace projections for constructing ensembles of classifiers. Information Sciences, 2012, 193(11): 1–21 Kuncheva L I, Rodriguez J J, Plumpton C O, Linden D E, Johnston SJ. Random subspace ensembles for FMRI classification. IEEE Transactions on Medical Imaging, 2010, 29(2): 531–542 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 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 Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory. 1998, 92–100 Wang J, Luo S W, Zeng XH. A random subspace method for co-training. In: Proceedings of 2008 IEEE International Joint Conference on Neural Networks. 2008, 195–200 Yaslan Y, Cataltepe Z. Co-training with relevant random subspaces. Neurocomputing, 2010, 73(10–12): 1652–1661 Zhang J, Zhang D. A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples. Pattern Recognition, 2011, 44(6): 1162–1171 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 Windeatt T, Duangsoithong R, Smith R. Embedded feature ranking for ensemble MLP classifiers. IEEE Transactions on Neural Networks, 2011, 22(6): 988–994 Rodriguez J J, Kuncheva L I, Alonso CJ. Rotation forest: a new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(10): 1619–1630 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 Amasyali M F, Ersoy OK. Classifier ensembles with the extended space forest. IEEE Transactions on Knowledge and Data Engineering, 2013, 26(3): 549–562 Polikar R, Depasquale J, Mohammed H S, Brown G, Kuncheva LI. Learn++.MF: a random subspace approach for the missing feature problem. Pattern Recognition, 2010, 43(11): 3817–3832 Nanni L, Lumini A. Evolved feature weighting for random subspace classifier. IEEE Transactions on Neural Networks, 2008, 19(2): 363–366 Kennedy J, Eberhart RC. A discrete binary version of the particle swarm optimization algorithm. Computational Cybernatics and Simulation, 1997, 5(1): 4104–4108 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 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 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 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 Dos Santos E M, Sabourin R, Maupin P. A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recognition, 2008, 41(10): 2993–3009 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 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 De Stefano C, Folino G, Fontanella F, Di Freca AS. Using bayesian networks for selecting classifiers in GP ensembles. Information Sciences, 2014, 258: 200–216 Rahman A, Verma B. Novel layered clustering-based approach for generating ensemble of classifiers. IEEE Transactions on Neural Networks, 2011, 22(5): 781–792 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 Zhang L, Suganthan PN. Oblique decision tree ensemble via multi-surface proximal support vector machine. IEEE Transactions on Cybernetics, 2015, 45(10): 2165–2176 Tan P J, Dowe DL. Decision forests with oblique decision trees. In: Proceedings of Mexican International Conference on Artificial Intelligence. 2006, 593–603 Zhou Z H, Wu J, Tang W. Ensembling neural networks: many could be better than all. Artificial Intelligence, 2002, 137(1–2): 239–263 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 Li H, Wen G, Yu Z, Zhou T. Random subspace evidence classifier. Neurocomputing, 2013, 110(13): 62–69 Hernández-Lobato D, Martínez-Muñoz G, Suárez A. How large should ensembles of classifiers be? Pattern Recognition, 2013, 46(5): 1323–1336 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 Kuncheva LI. A bound on kappa-error diagrams for analysis of classifier ensembles. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 494–501 Gao W, Zhou ZH. Approximation stability and boosting. In: Proceedings of International Conference on Algorithmic Learning Theory. 2010, 59–73 Yin X C, Huang K, Hao H W, Iqbal K, Wang ZB. A novel classifier ensemble method with sparsity and diversity. Neurocomputing, 2014, 134: 214–221 Zhang L, Suganthan PN. Random forests with ensemble of feature spaces. Pattern Recognition, 2014, 47(10): 3429–3437 Li N, Yu Y, Zhou ZH. Diversity regularized ensemble pruning. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2012, 330–345 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 Zhou Z H, Li N. Multi-information ensemble diversity. In: Proceedings of International Workshop on Multiple Classifier Systems. 2010, 134–144 Sun T, Zhou ZH. Structural diversity for decision tree ensemble learning. Frontiers of Computer Science, 2018, 12(3): 560–570 Mao S, Jiao L, Xiong L, Gou S, Chen B, Yeung SK. Weighted classifier ensemble based on quadratic form. Pattern Recognition, 2015, 48(5): 1688–1706 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 Kim K J, Cho SB. An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Transactions on Evolutionary Computation, 2008, 12(3): 377–388 Qian C, Yu Y, Zhou ZH. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015 Zhou Z H, Feng J. Deep forest: towards an alternative to deep neural networks. 2017, arXiv preprint arXiv:1702.08835 Feng J, Zhou ZH. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018 Zhang Y L, Zhou J, Zheng W, Feng J, Li L, Liu Z, Zhou ZH. Distributed deep forest and its application to automatic detection of cash-out fraud. 2018, arXiv preprint arXiv:1805.04234 Feng J, Yu Y, Zhou ZH. Multi-layered gradient boosting decision trees. In: Proceedings of Advances in Neural Information Processing Systems. 2018, 3555–3565 Pang M, Ting K M, Zhao P, Zhou ZH. Improving deep forest by confidence screening. In: Proceedings of the 18th IEEE International Conference on Data Mining. 2018, 1194–1199 Yu Z, Li L, Liu J, Han G. Hybrid adaptive classifier ensemble. IEEE Transactions on Cybernetics, 2015, 45(2): 177–190 Zhou Z H, Zhang ML. Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowledge and Information Systems, 2007, 11(2): 155–170 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 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 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 Xiao J, He C, Jiang X, Liu D. A dynamic classifier ensemble selection approach for noise data. Information Sciences, 2010, 180(18): 3402–3421 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 Liu X Y, Wu J, Zhou ZH. Exploratory under-sampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009, 39(2): 539–550 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 Li Q, Li G, Niu W, Cao Y, Chang L, Tan J, Guo L. Boosting imbal-anced data learning with wiener process oversampling. Frontiers of Computer Science, 2017, 11(5): 836–851 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 Li N, Zhou ZH. Selective ensemble of classifier chains. In: Proceedings of International Workshop on Multiple Classifier Systems. 2013, 146–156 Li N, Jiang Y, Zhou ZH. Multi-label selective ensemble. In: Proceedings of International Workshop on Multiple Classifier Systems. 2015, 76–88 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 Yu D J, Hu J, Yang J, Shen H B, Tang J, Yang JY. Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013, 10(4): 994–1008 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 Daliri MR. Combining extreme learning machines using support vector machines for breast tissue classification. Computer Methods in Biomechanics and Biomedical Engineering, 2015, 18(2): 185–191 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 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 Zhang B. Reliable classification of vehicle types based on cascade classifier ensembles. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 322–332 Sun S, Zhang C. The selective random subspace predictor for traffic flow forecasting. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2): 367–373 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 Zhang P, Bui T D, Suen CY. A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition, 2007, 40(12): 3415–3429 Xu X S, Xue X, Zhou ZH. Ensemble multi-instance multi-label learning approach for video annotation task. In: Proceedings of the 19th ACM International Conference on Multimedia. 2011, 1153–1156 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 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 Tao D, Tang X, Li X, Wu X. Asymmetric bagging and random sub-space for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(7): 1088–1099 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 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 Yu L, Wang S, Lai KK. 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 Fersini E, Messina E, Pozzi FA. Sentiment analysis: Bayesian ensemble learning. Decision Support Systems, 2014, 68: 26–38 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 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 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 Lu X, Zhang J, Li T, Zhang Y. Hyperspectral image classification based on semi-supervised rotation forest. Remote Sensing, 2017, 9(9): 924 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 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 Woo H, Park CH. Semi-supervised ensemble learning using label propagation. In: Proceedings of the 12th IEEE International Conference on Computer and Information Technology. 2012, 421–426 Zhang M L, Zhou ZH. Exploiting unlabeled data to enhance ensemble diversity. Data Mining and Knowledge Discovery, 2013, 26(1): 98–129 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 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 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 Wang Y, Li T. Improving semi-supervised co-forest algorithm in evolving data streams. Applied Intelligence, 2018, 48(10): 3248–3262 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 Li M, Zhou ZH. 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 Guz U, Cuendet S, Hakkani-Tur D, Tur G. Multi-view semi-supervised learning for dialog act segmentation of speech. IEEE Transactions on Audio Speech and Language Processing, 2010, 18(2): 320–329 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 Abdelgayed T S, Morsi W G, Sidhu TS. Fault detection and classification based on co-training of semi-supervised machine learning. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1595–1605 Saydali S, Parvin H, Safaei AA. Classifier ensemble by semi-supervised learning: local aggregation methodology. In: Proceedings of International Doctoral Workshop on Mathematical and Engineering Methods in Computer Science. 2015, 119–132 Shao W, Tian X. Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development. Neuro-computing, 2017, 222: 91–104 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 Strehl A, Ghosh J. Cluster ensembles: a knowledge reuse framework for combining partitionings. Journal of Machine Learning Research, 2002, 3(3): 583–617 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 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 Franek L, Jiang X. Ensemble clustering by means of clustering embedding in vector spaces. Pattern Recognition, 2014, 47(2): 833–842 Yu Z, Wong H S, Wang H. Graph-based consensus clustering for class discovery from gene expression data. Bioinformatics, 2007, 23(21): 2888–2896 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 Yu Z, Li L, You J, Wong H S, Han G. SC3: triple spectral clustering-based consensus clustering framework for class discovery from cancer gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012, 9(6): 1751–1765 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 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 Ayad H G, Kamel MS. On voting-based consensus of cluster ensembles. Pattern Recognition, 2010, 43(5): 1943–1953 Zhang S, Wong H S, Shen Y. Generalized adjusted rand indices for cluster ensembles. Pattern Recognition, 2012, 45(6): 2214–2226 Fred A L N, Jain AK. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(6): 835–850 Lourenco A, Fred A L N, Jain AK. On the scalability of evidence accumulation clustering. In: Proceedings of the 20th International Conference on Pattern Recognition. 2010, 782–785 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 Fern X Z, Brodley CE. 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 Kuncheva L I, Whitaker CJ. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 2003, 51(2): 181–207 Kuncheva L I, Vetrov DP. 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 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 Topchy A P, Law M H C, Jain A K, Fred AL. Analysis of consensus partition in cluster ensemble. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM’04). 2004, 225–232 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 Hore P, Hall L O, Goldgof DB. A scalable framework for cluster ensembles. Pattern Recognition, 2009, 42(5): 676–688 Fern X Z, Lin W. Cluster ensemble selection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2008, 1(3): 128–141 Azimi J, Fern X. Adaptive cluster ensemble selection. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence. 2009, 992–997 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 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 Yu Z, You J, Wong H S, Han G. From cluster ensemble to structure ensemble. Information Sciences, 2012, 198: 81–99 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 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 Yang Y, Jiang J. HMM-based hybrid meta-clustering ensemble for temporal data. Knowledge-Based Systems, 2014, 56: 299–310 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 Yu Z, Wong HS. Class discovery from gene expression data based on perturbation and cluster ensemble. IEEE Transactions on Nanobio-science, 2009, 8(2): 147–160 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 Bioinfor-matics, 2015, 12(4): 887–901 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 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 Alush A, Goldberger J. Ensemble segmentation using efficient integer linear programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1966–1977 Li H, Meng F, Wu Q, Luo B. Unsupervised multiclass region coseg-mentation via ensemble clustering and energy minimization. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(5): 789–801 Zhang X, Jiao L, Liu F, Bo L, Gong M. Spectral clustering ensemble applied to SAR image segmentation. IEEE Transactions on Geo-science and Remote Sensing, 2008, 46(7): 2126–2136 Jia J, Liu B, Jiao L. Soft spectral clustering ensemble applied to image segmentation. Frontiers of Computer Science, 2011, 5(1): 66–78 Rafiee G, Dlay S S, Woo WL. 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 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 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 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 Tsai C F, Hung C. Cluster ensembles in collaborative filtering recommendation. Applied Soft Computing, 2012, 12(4): 1417–1425 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 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 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 Karypis G, Han E H S, Kumar V. Chameleon: hierarchical clustering using dynamic modeling. Computer, 1999, 32(8): 68–75 Xiao W, Yang Y, Wang H, Li T, Xing H. Semi-supervised hierarchical clustering ensemble and its application. Neurocomputing, 2016, 173: 1362–1376 Zhou Z H, Tang W. Clusterer ensemble. Knowledge-Based Systems, 2006, 19(1): 77–83 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 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 Wang H, Yang D, Qi J. Semi-supervised cluster ensemble based on normal mutual information. Energy Procedia, 2011, 13: 1673–1677 Yu Z, Luo P, Liu J, Wong H S, You J, Han G, Zhang J. Semi-supervised ensemble clustering based on selected constraint projection. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(12): 2394–2407 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 Iqbal A M, Moh’D A, Khan Z. Semi-supervised clustering ensemble by voting. Computer Science, 2012, 2(9): 33–40 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 Yan B, Domeniconi C. Subspace metric ensembles for semi-supervised clustering of high dimensional data. In: Proceedings of European Conference on Machine Learning. 2006, 509–520 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 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 Junaidi A, Fink GA. 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 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 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 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 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 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 Liu W, Zhang M, Luo Z, Cai Y. An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors. IEEE Access, 2017, 5: 24417–24425 Kandaswamy C, Silva L M, Alexandre L A, Santos JM. Deep transfer learning ensemble for classification. In: Proceedings of International Work-Conference on Artificial Neural Networks. 2015, 335–348 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 Liu X, Liu Z, Wang G, Cai Z, Zhang H. Ensemble transfer learning algorithm. IEEE Access, 2018, 6: 2389–2396 Brys T, Harutyunyan A, Vrancx P, Nowé A, Taylor ME. Multi-objectivization and ensembles of shapings in reinforcement learning. Neurocomputing, 2017, 263: 48–59 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