On the Power of Ensemble: Supervised and Unsupervised Methods Reconciled Tutorial on SIAM Data Mining Conference (SDM), Columbus, OH, May 1 2010 Jing Gao, Wei Fan and Jiawei Han Abstract Ensemble methods have emerged as a powerful method for improving the robustness as well as the accuracy of both supervised and unsupervised solutions. Moreover, as enormous amounts of data are continuously generated from different views, it is important to consolidate different concepts for intelligent decision making. In the past decade, there have been numerous studies on the problem of combining competing models into a committee, and the success of ensemble techniques has been observed in multiple disciplines, including recommendation systems, anomaly detection, stream mining, and web applications. The ensemble techniques have been mostly studied in supervised and unsupervised learning communities separately. However, they share the same basic principles, i.e., combination of diversified base models strengthens weak models. Also, when both supervised and unsupervised models are available for a single task, merging all of the results leads to better performances. Therefore, there is a need of a systematic introduction and comparison of the ensemble techniques, combining the views of both supervised and unsupervised learning ensembles. In this tutorial, we will present an organized picture on ensemble methods with a focus on the mechanism to merge the results. We start with the description and applications of ensemble methods. Through reviews of well-known and state-of-the-art ensemble methods, we show that supervised learning ensembles usually learn" this mechanism based on the available labels in the training data, whereas unsupervised ensembles simply combine multiple clustering solutions based on consensus". We end the tutorial with a systematic approach to combine both supervised and unsupervised models, and several applications of ensemble methods. Outline
References [AUL08] M. Amini, N. Usunier, and F. Laviolette. A transductive bound for the voted classifier with an application to semi-supervised learning. In Advances in Neural Information Processing Systems 21, 2008. [BBY04] M. Balcan, A. Blum, and K. Yang. Co-training and expansion: Towards bridging theory and practice. In Advances in Neural Information Processing Systems 17, 2004. [BBM07] A. Banerjee, S. Basu, and S. Merugu. Multi-way clustering on relation graphs. In Proc. 2007 SIAM Int. Conf. Data Mining (SDM'07), 2007. [BaKo04] E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-139, 2004. [BEM05] R. Bekkerman, R. El-Yaniv, and A. McCallum. Multi-way distributional clustering via pairwise interactions. In Proc. 2005 Int. Conf. Machine Learning (ICML'05), pages 41-48, 2005. [BDH05] P. N. Bennett, S. T. Dumais, and E. Horvitz. The combination of text classifiers using reliability indicators. Information Retrieval, 8(1):67-100, 2005. [BiSc04] S. Bickel and T. Scheffer. Multi-view clustering. In Proc. 2004 Int. Conf. Data Mining (ICDM'04), pages 19-26, 2004. [BlMi98] A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. Proceedings of the Workshop on Computational Learning Theory, pages 92-100, 1998. [BGS+08] P. Brazdil, C. Giraud-Carrier, C. Soares, and R. Vilalta. Metalearning: Applications to Data Mining. Springer, 2008. [BBS05] Ulf Brefeld, Christoph B¨¹scher, and Tobias Scheffer. Multi-view discriminative sequential learning. In Proc. European Conf. Machine Learning (ECML'05), pages 60-71, 2005. [Breiman96] L. Breiman. Bagging predictors. Machine Learning, 26:123-140, 1996. [Breiman01] L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001. [Caruana97] R. Caruana. Multitask learning. Machine Learning, 28(1):41-75, 1997. [CoSi99] M. Collins and Y. Singer. Unsupervised models for named entity classification. In Proc. 1999 Conf. Empirical Methods in Natural Language Processing (EMNLP'99), 1999. [CKW08] K. Crammer, M. Kearns, and J. Wortman. Learning from multiple sources. Journal of Machine Learning Research, 9:1757-1774, 2008. [DYX+07] W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Boosting for transfer learning. In Proc. 2007 Int. Conf. Machine Learning (ICML'07), pages 193-200, 2007. [DLM01] S. Dasgupta, M. Littman, and D. McAllester. PAC Generalization Bounds for Co-training. In Advances in Neural Information Processing Systems 14, 2001. [DaFa06] I. Davidson and W. Fan. When efficient model averaging out-performs boosting and bagging. In Proc. 2006 European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD'06), pages 478-486, 2006. [DMM03] I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'03), pages 89-98, 2003.
[Dietterich00] T. Dietterich.
Ensemble methods in machine learning. In Proc. 2000 Int.
Workshop Multiple Classifier Systems, pages 1-15, 2000. [DWH01] E. Dimitriadou, A. Weingessel, and K. Homik. Voting-merging: an ensemble method for clustering. In Proc. 2001 Int. Conf. Artificial Neural Networks (ICANN'01), pages 217-224, 2001. [DoAl09] C. Domeniconi and M. Al-Razgan. Weighted cluster ensembles: Methods and analysis. ACM Transactions on Knowledge Discovery from Data (TKDD), 2(4):1-40, 2009. [Domingos00] P. Domingos. Bayesian averaging of classifiers and the overfitting problem. In Proc. 2000 Int. Conf. Machine Learning (ICML'00), pages 223-230, 2000. [DHS01] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. [DzZe02] S. Dzeroski and B. Zenko. Is combining classifiers better than selecting the best one. In Proc. 2002 Int. Conf. Machine Learning (ICML'02), pages 123-130, 2002. [DuFr03] S. Dudoit and J. Fridlyand. Bagging to improve the accuracy of a clustering procedure. Bioinformatics, 19(9): 1090-1099, 2003. [FaDa07] W. Fan and I. Davidson. On sample selection bias and its efficient correction via model averaging and unlabeled examples. In Proc. 2007 SIAM Int. Conf. Data Mining (SDM'07), 2007. [FGM+05] W. Fan, E. Greengrass, J. McCloskey, P. S. Yu, and K. Drummey. Effective estimation of posterior probabilities: Explaining the accuracy of randomized decision tree approaches. In Proc. 2005 Int. Conf. Data Mining (ICDM'05), pages 154-161, 2005. [FHM+05] J. Farquhar, D. Hardoon, H. Meng, J. Shawe-taylor, and S. Szedmak. Two view learning: SVM-2K, theory and practice. In Advances in Neural Information Processing Systems 18, 2005. [FeBr04] X. Z. Fern and C. E. Brodley. Solving cluster ensemble problems by bipartite graph partitioning. In Proc. 2004 Int. Conf. Machine Learning (ICML'04), pages 281-288, 2004. [FeLi08] X. Z. Fern and W. Lin. Cluster ensemble selection. In Proc. 2008 SIAM Int. Conf. Data Mining (SDM'08), 2008. [FiSk03] V. Filkov and S. Skiena. Integrating microarray data by consensus clustering. In Proc. 2003 Int. Conf. Tools with Artificial Intelligence, pages 418-426, 2003. [FrJa02] A. Fred and A. Jain. Data Clustering using evidence accumulation. In Proc. 2002 Int. Conf. Pattern Recognition (ICPR'02), 2002. [FrSc97] Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997. [FrPo08] J. H. Friedman and B. E. Popescu. Predictive learning via rule ensembles. Annals of Applied Statistics, 3(2):916-954, 2008. [GGB+08] K. Ganchev, J. Graca, J. Blitzer, and B. Taskar. Multi-view learning over structured and non-identical outputs. In Proc. 2008 Conf. Uncertainty in Artificial Intelligence (UAI'08), pages 204-211, 2008. [GFH07] J. Gao, W. Fan, and J. Han. On appropriate assumptions to mine data streams: Analysis and practice. In Proc. 2007 Int. Conf. Data Mining (ICDM'07), pages 143-152, 2007. [GFJ+08] J. Gao, W. Fan, J. Jiang, and J. Han. Knowledge transfer via multiple model local structure mapping. In Proc. 2008 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'08), pages 283-291, 2008. [GFS+09] J. Gao, W. Fan, Y. Sun, and J. Han. Heterogeneous source consensus learning via decision propagation and negotiation. In Proc. 2009 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'09), pages 339-347, 2009. [GLF+09] J. Gao, F. Liang, W. Fan, Y. Sun, and J. Han. Graph-based consensus maximization among multiple supervised and unsupervised models. In Advances in Neural Information Processing Systems 22, 2009. [GSI+09] R. Ghaemi, M. Sulaiman, H. Ibrahim, and N. Mutspha. A survey: clustering ensembles techniques. World Academy of Science, Engineering and Technology 50, 2009. [GeTa07] L. Getoor and B. Taskar. Introduction to statistical relational learning. MIT Press, 2007. [GMT07] A. Gionis, H. Mannila, and P. Tsaparas. Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 2007. [GVB04] C. Giraud-Carrier, R. Vilalta, and P. Brazdil. Introduction to the special issue on meta-learning. Machine Learning, 54(3):187-193, 2004. [GoFi08] A. Goder and V. Filkov. Consensus clustering algorithms: comparison and refinement. In Proc. 2008 Workshop on Algorithm Engineering and Experiments (ALENEX'08), pages 109-117, 2008. [GoZh00] S. Goldman and Y. Zhou. Enhancing supervised learning with unlabeled data. In Proc. 2000 Int. Conf. Machine Learning (ICML'00), pages 327-334, 2000.
[HKT06] S. T. Hadjitodorov,
L. I. Kuncheva, and L. P. Todorova. Moderate diversity
for better cluster ensembles. Information Fusion,
7(3):264-275, 2006. [HaKa06] J. Han and M. Kamber. Data mining: concepts and techniques. Morgan Kaufmann, second edition, 2006. [HTF09] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, second edition, 2009. [HMR+99] J. Hoeting, D. Madigan, A. Raftery, and C. Volinsky. Bayesian model averaging: a tutorial. Statistical Science, 14:382-417, 1999. [JJN+91] R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991. [KoMa] J. Kolter and M. Maloof. Using additive expert ensembles to cope with concept drift. In Proc. 2005 Int. Conf. Machine Learning (ICML'05), pages 449-456, 2005. [KuWh03] L. I. Kuncheva and C. J. Whitaker. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning, 51(2):181-207, 2003. [Leskes05] B. Leskes. The Value of Agreement, a New Boosting Algorithm. In 2005 Proc. Conf. Learning Theory (COLT'05), pages 95-110, 2005. [LiDi08] T. Li and C. Ding. Weighted consensus clustering. In Proc. 2008 SIAM Int. Conf. Data Mining (SDM'08), 2008. [LDJ07] T. Li, C. Ding, and M. Jordan. Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. In Proc. 2007 Int. Conf. Data Mining (ICDM'07), pages 577-582, 2007. [LiOg05] T. Li and M. Ogihara. Semisupervised learning from different information sources. Knowledge and Information Systems, 7(3):289-309, 2005. [LiYa06] C. X. Ling and Q. Yang. Discovering classification from data of multiple sources. Data Mining and Knowledge Discovery, 12(2-3):181-201, 2006. [LZY05] B. Long, Z. Zhang, and P. S. Yu. Combining multiple clusterings by soft correspondence. In Proc. 2005 Int. Conf. Data Mining (ICDM'05), pages 282-289, 2005. [LZX+08] P. Luo, F. Zhuang, H. Xiong, Y. Xiong, and Q. He. Transfer learning from multiple source domains via consensus regularization. In Proc. 2008 Int. Conf. Information and Knowledge Management (CIKM'08), pages 103-112, 2008. [MTP04] B. Minaei-Bidgoli, A. Topchy, and W. Punch: A comparison of resampling methods for clustering ensembles. In Proc. 2004 Int. Conf. Artificial Intelligence (ICAI'04), pages 939-945, 2004. [NiGh00] K. Nigam and R. Ghani. Analyzing the effectiveness and applicability of co-training. In Proc. 2000 Int. Conf. Information and Knowledge Management (CIKM'00), pages 86-93, 2000. [OkVa08] O. Okun and G. Valentini. Supervised and Unsupervised Ensemble Methods and their Applications. Springer, 2008. [Polikar06] R. Polikar. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3):21-45, 2006.
[PrSc08] C.
Preisach and L. Schmidt-Thieme. Ensembles of
relational classifiers. Knowledge and
Information Systems, 14(3):249-272, 2008. [PTJ05] W. Punch, A. Topchy, and A. K. Jain. Clustering ensembles: Models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12):1866-1881, 2005. [PuGh08] K. Punera and J. Ghosh. Consensus based ensembles of soft clusterings. Applied Artificial Intelligence, 22(7-8): 780-810, 2008. [RoKa07] D. M. Roy and L. P. Kaelbling. Efficient bayesian task-level transfer learning. In Proc. 2007 Int. Joint Conf. Artificial Intelligence (IJCAI'07), pages 2599-2604, 2007. [SNB05] V. Sindhwani, P. Niyogi, and M. Belkin. A co-regularization approach to semi-supervised learning with multiple views. In Proc. 2005 ICML workshop on Learning with Multiple Views, 2005. [SMP+07] V. Singh, L. Mukherjee, J. Peng, and J. Xu. Ensemble clustering using semidefinite programming. In Advances in Neural Information Processing Systems 20, 2007.
[StGh03] A. Strehl and J. Ghosh. Cluster
ensembles --a knowledge reuse framework for combining multiple
partitions. Journal of Machine Learning Research, 3:583-617, 2003.
[TLJ+04] A. Topchy, M. Law, A. Jain, and A. Fred. Analysis of consensus partition in cluster ensemble. In Proc. 2004 Int. Conf. Data Mining (ICDM'04), pages 225-232, 2004. [TuGh96] K. Tumer and J. Ghosh. Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition, 29, 1996. [ViDr02] R. Vilalta and Y. Drissi. A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2):77-95, 2002. [WFY+03] H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'03), pages 226-235, 2003. [WSB09] H. Wang, H. Shan, and A. Banerjee. Bayesian cluster ensembles. In Proc. 2009 SIAM Int. Conf. Data Mining (SDM'09), 2009. [Wolpert92] D. H. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992. [WWL09] F. Wang, X. Wang, and T. Li.Generalized Cluster aggregation. In Proc. 2009 Int. Joint Conf. Artificial Intelligence (IJCAI'09), pages 1279-1284, 2009. [ZGY05] J. Zhang, Z. Ghahramani, and Y. Yang. Learning multiple related tasks using latent independent component. In Advances in Neural Information Processing Systems 18, 2005. [ZFY+06] K. Zhang, W. Fan, X. Yuan, I. Davidson, and X. Li. Forecasting skewed biased stochastic ozone days: Analyses and solutions. In Proc. 2006 Int. Conf. Data Mining (ICDM'06), pages 753-764, 2006. [ZZY07] Z. Zhou, D. Zhan, and Q. Yang. Semi-Supervised Learning with Very Few Labeled Training Examples. In Proc. 2007 Conf. Artificial Intelligence (AAAI'07), pages 675-680, 2007.
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