KDD Cup 2001 report

ABSTRACT

This paper presents results and lessons from KDD Cup 2001. KDD Cup 2001 focused on mining biological databases. It involved three cutting-edge tasks related to drug design and genomics.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 2: Cheng, J. (1998). PowerConstructor System. http://www.cs.ualberta.ca/~jcheng/bnpc.htm.
• 3: Cheng, J. (2000). PowerPredictor System. http://www.cs.ualberta.ca/~jcheng/bnpp.htm.
• 4: Cheng, J., Greiner, R. (1999). Comparing Bayesian network classifiers. In UAI-99.
• 5: Jie Cheng , Russell Greiner, Learning Bayesian Belief Network Classifiers: Algorithms and System, Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence, p.141-151, June 07-09, 2001
• 6: Jie Cheng , Russell Greiner , Jonathan Kelly , David Bell , Weiru Liu, Learning Bayesian networks from data: an information-theory based approach, Artificial Intelligence, v.137 n.1-2, p.43-90, May 2002 [doi>10.1016/S0004-3702(02)00191-1]
• 7: Gregory F. Cooper , Edward Herskovits, A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning, v.9 n.4, p.309-347, Oct. 1992 [doi>10.1023/A:1022649401552]
• 8: Yoav Freund , Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences, v.55 n.1, p.119-139, Aug. 1997 [doi>10.1006/jcss.1997.1504]
• 9: David J. Hand , Padhraic Smyth , Heikki Mannila, Principles of data mining, MIT Press, Cambridge, MA, 2001
• 10: Hanley, J. A. and McNeil B. J. (1982). The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology, 143, pp. 29-36.
• 11: Heckerman, D. (1995). A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06. Microsoft Research.
• 12: Thorsten Joachims, Making large-scale support vector machine learning practical, Advances in kernel methods: support vector learning, MIT Press, Cambridge, MA, 1999
• 13: Mark-A. Krogel , Stefan Wrobel, Transformation-Based Learning Using Multirelational Aggregation, Proceedings of the 11th International Conference on Inductive Logic Programming, p.142-155, September 09-11, 2001
• 14: Morishita, S. Computing optimal hypotheses efficiently for boosting. Springer LNAI: Progresses in Discovery Science, in press.
• 15: Shinichi Morishita , Jun Sese, Transversing itemset lattices with statistical metric pruning, Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.226-236, May 15-18, 2000, Dallas, Texas, United States [doi>10.1145/335168.335226]
• 16: Richard E. Neapolitan, Probabilistic reasoning in expert systems: theory and algorithms, John Wiley & Sons, Inc., New York, NY, 1990
• 17: Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
• 18: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 19: Spirtes, P., Glymour, C. and Scheines, R. (1993). Causation, Prediction ,and Search. Springer Lecture Notes in Statistics.
• 20: Stefan Wrobel, Inductive logic programming for knowedge discovery in databases, Relational Data Mining, Springer-Verlag New York, Inc., New York, NY, 2001

INDEX TERMS

Keywords:
Competition, biology, drug design, genomics