Data Mining and Machine Learning Papers

Below are select papers on a variety of topics. The list is not meant to be exhaustive. The papers found on this page either relate to my research interests of are used when I teach courses on machine learning or data mining.

• General (articles)
  • Data Mining and Statistics: What's the Connection?
  • Data Mining: Statistics and More?, D. Hand, American Statistician, 52(2):112-118.
  • Data Mining, G. Weiss and B. Davison, in Handbook of Technology Management, John Wiley and Sons, expected 2010.
  • From Data Mining to Knowledge Discovery in Databases, U. Fayyad, G. Piatesky-Shapiro & P. Smyth, AI Magazine, 17(3):37-54, Fall 1996.
  • Mining Business Databases, Communications of the ACM, 39(11): 42-48.
  • 10 Challenging Problems in Data Mining Research, Q. Yiang and X. Wu, International Journal of Information Technology & Decision Making, Vol. 5, No. 4, 2006, 597-604. (slides)

• General (short news articles)
  • The Long Tail, by Anderson, C., Wired magazine.
  • AOL's Disturbing Glimpse Into Users' Lives, by McCullagh, D., News.com, August 9, 2006

• General Data Mining Methods and Algorithms
  • Top 10 Algorithms in Data Mining, X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. motoda, G.J. MClachlan, A. Ng, B. Liu, P.S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, Knowl Inf Syst (2008) 141-37.
  • Induction of Decision Trees, R. Quinlan, Machine Learning, 1(1):81-106, 1986.

• Web and Link Mining
  • The Pagerank Citation Ranking: Bringing Order to the Web, L. Page, S. Brin, R. Motwani, T. Winograd, Technical Report, Stanford University, 1999.
  • The Structure and Function of Complex Networks, M. E. J. Newman, SIAM Review, 2003, 45, 167-256.
  • Link Mining: A New Data Mining Challenge, L. Getoor, SIGKDD Explorations, 2003, 5(1), 84-89.
  • Link Mining: A Survey, L. Getoor, SIGKDD Explorations, 2005, 7(2), 3-12.
• Semi-supervised Learning
  • Semi-Supervised Learning Literature Survey, X. Zhu, Computer Sciences TR 1530, University of Wisconsin -- Madison.
  • Introduction to Semi-Supervised Learning, in Semi-Supervised Learning (Chapter 1) O. Chapelle, B. Scholkopf, A. Zien (eds.), MIT Press, 2006. (Fordham's library has online access to the entire text)
  • Learning with Labeled and Unlabeled Data, M. Seeger, University of Edinburgh (unpublished), 2002.
  • Person Identification in Webcam Images: An Application of Semi-Supervised Learning, M. Balcan, A. Blum, P. Choi, J. lafferty, B. Pantano, M. Rwebangira, X. Zhu, Proceedings of the 22nd ICML Workshop on Learning with Partially Classified Training Data, 2005.
  • Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains, N. Chawla, G. Karakoulas, Journal of Artificial Intelligence Research, 23:331-366, 2005.
  • Text Classification from Labeled and Unlabeled Documents using EM, K. Nigam, A. McCallum, S. Thrun, T. Mitchell, Machine Learning, 39, 103-134, 2000.
  • Self-taught Learning: Transfer Learning from Unlabeled Data, R. Raina, A. Battle, H. Lee, B. Packer, A. Ng, in Proceedings of the 24th International Conference on Machine Learning, 2007.
  • An iterative algorithm for extending learners to a semisupervised setting, M. Culp, G. Michailidis, 2007 Joint Statistical Meetings (JSM), 2007
• Partially-Supervised Learning / Learning with Uncertain Class Labels
  • Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers, V. Sheng, F. Provost, P. Ipeirotis, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
  • Logistic Regression for Partial Labels, in 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Volume III, pp. 1935-1941, 2002.
  • Classification with Partial labels, N. Nguyen, R. Caruana, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
  • Imprecise and Uncertain Labelling: A Solution based on Mixture Model and Belief Functions, E. Come, 2008 (powerpoint slides).
  • Induction of Decision Trees from Partially Classified Data Using Belief Functions, M. Bjanger, Norweigen University of Science and Technology, 2000.
  • Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth, P. Smyth, M. Burl, U. Fayyad, P. Perona, KDD Workshop 1994, AAAI Technical Report WS-94-03, pp. 109-120, 1994.
• Recommender Systems
  • Trust No One: Evaluating Trust-based Filtering for Recommenders, J. O'Donovan and B. Smyth, In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), 2005, 1663-1665.
  • Trust in Recommender Systems, J. O'Donovan and B. Symyth, In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI-05), 2005, 167-174.
• Rarity and Class Imbalance

  General resources available on this topic:

  • There is a bibliography of papers on this topic, but it has not been updated since 2004
  • ICML 2003 Workshop: Learning from Imbalanced Data Sets II
  • AAAI '2000 Workshop on Learning from Imbalanced Data Sets

  Papers

  • A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, G. Batista, R. Prati, and M. Monard, SIGKDD Explorations, 6(1):20-29, 2004.
  • Class Imbalance versus Small Disjuncts, T. Jo and N. Japkowicz, SIGKDD Explorations, 6(1): 40-49, 2004.
  • Extreme Re-balancing for SVMs: a Case Study, B. Raskutti and A. Kowalczyk, SIGKDD Explorations, 6(1):60-69, 2004.
  • A Multiple Resampling Method for Learning from Imbalanced Data Sets, A. Estabrooks, T. Jo, and N. Japkowicz, in Computational Intelligence, 20(1), 2004.
  • SMOTE: Synthetic Minority Over-sampling Technique, N. Chawla, K. Boyer, L. Hall, and W. Kegelmeyer, Journal of Articifial Intelligence Research, 16:321-357.
  • Generative Oversampling for Mining Imbalanced Datasets, A. Liu, J. Ghosh, and C. Martin, Third International Conference on Data Mining (DMIN-07), 66-72.
  • Learning from Little: Comparison of Classifiers Given Little of Classifiers given Little Training, G. Forman and I. Cohen, in 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, 161-172, 2004.
  • Issues in Mining Imbalanced Data Sets - A Review Paper, S. Visa and A. Ralescu, in Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference, pp. 67-73, 2005.
  • Wrapper-based Computation and Evaluation of Sampling Methods for Imbalanced Datasets, N. Chawla, L. Hall, and A. Joshi, in Proceedings of the 1st International Workshop on Utility-based Data Mining, 24-33, 2005.
  • C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling, C. Drummond and R. Holte, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.
  • C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure, N. Chawla, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.
  • Class Imbalances: Are we Focusing on the Right Issue?, N. Japkowicz, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.
  • Learning when Data Sets are Imbalanced and When Costs are Unequal and Unknown, M. Maloof, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.
  • Uncertainty Sampling Methods for One-class Classifiers, P. Juszcak and R. Duin, in ICML Workshop on Learning from Imbalanced Datasets II, 2003.

• Active Learning
  • Improving Generalization with Active Learning, D Cohn, L. Atlas, and R. Ladner, Machine Learning 15(2), 201-221, May 1994.
  • On Active Learning for Data Acquisition, Z. Zheng and B. Padmanabhan, In Proc. of IEEE Intl. Conf. on Data Mining, 2002.
  • Active Sampling for Class Probability Estimation and Ranking, M. Saar-Tsechansky and F. Provost, Machine Learning 54:2 2004, 153-178.
  • The Learning-Curve Sampling Method Applied to Model-Based Clustering, C. Meek, B. Thiesson, and D. Heckerman, Journal of Machine Learning Research 2:397-418, 2002.
  • Active Sampling for Feature Selection, S. Veeramachaneni and P. Avesani, Third IEEE Conference on Data Mining, 2003.
  • Heterogeneous Uncertainty Sampling for Supervised Learning, D. Lewis and J. Catlett, In Proceedings of the 11th International Conference on Machine Learning, 148-156, 1994.
  • Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction, G. Weiss and F. Provost, Journal of Artificial Intelligence Research, 19:315-354, 2003.
  • Active Learning using Adaptive Resampling, KDD 2000, 91-98.

• Cost-Sensitive Learning
  • Types of Cost in Inductive Concept Learning, P. Turney, In Proceedings Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning.
  • Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection, P. Chan and S. Stolfo, KDD 1998.