High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application

• Aha, D., Kibler, D., and Albert, M. 1991. Instance-based learning algorithms. Machine Learning, 6:37–66.

  Google Scholar 

• Auvil, L., Redman, T., Tcheng, D., and Welge, M. 1999. Data to Knowledge (D2K): A Rapid Application Development Environment for Knowledge Discovery. NCSA Technical Report, URL: http://archive.ncsa.uiuc.edu/STI/ALG/d2k.

• Benjamin, D.P. (Ed.) 1990. Change of Representation and Inductive Bias. Boston: Kluwer Academic Publishers.

  Google Scholar 

• Brooks, F.P., Jr. 1995. The Mythical Man-Month: Essays on Software Engineering, 20th Anniversary Edition. Reading, MA: Addison-Wesley.

  Google Scholar 

• Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., and Freeman, D. 1988. AUTOCLASS: A bayesian classification system. In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), pp. 54–64.

• Cherkauer, K.J. and Shavlik, J.W. 1996. Growing simpler decision trees to facilitiate knowledge discovery. In Proceedings of the Second International Conference of Knowledge Discovery and Data Mining (KDD-96): Portland, OR.

• Dempster, A., Laird, N., and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(Series B):1–38.

  Google Scholar 

• Donoho, S.K. 1996. Knowledge-Guided Constructive Induction. PhD Thesis, University of Illinois at Urbana-Champaign (Technical Report UIUC-DCS-R1970).

  Google Scholar 

• Dejong, K.A., Spears, W.M., and Gordon, D.F. 1993. Using genetic algorithms for concept learning. Machine Learning, 13:161–188.

  Google Scholar 

• Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. 1996. Knowledge Discovery and Data Mining: Towards a Unifying Framework. In Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., editors, Advances in Knowledge Discovery Data Mining, pp. 82–88. Cambridge, MA: MIT Press.

  Google Scholar 

• Gersho, A. and Gray, R.M. 1992. Vector Quantization and Signal Compression. Norwell, MA: Kluwer Academic Publishers.

  Google Scholar 

• Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley.

  Google Scholar 

• Grefenstette, J.J. 1990. Genesis Genetic Algorithm Package.

• Haykin, S. 1999. Neural Networks: A Comprehensive Foundation, 2nd edn. Englewood Cliffs, NJ: Prentice Hall.

  Google Scholar 

• Hsu, W.H., Welge, M., Wu, J., and Yang, T. 1999. Genetic algorithms for selection and partitioning of attributes in large-scale data mining problems. In Proceedings of the Joint AAAI-GECCO Workshop on Data Mining and Evolutionary Algorithms, Orlando, FL.

• Hsu, W.H. 1998. Time series learning with probabilistic network composites. PhD Thesis, University of Illinois at Urbana-Champaign (Technical Report UIUC-DCS-R2063).

  Google Scholar 

• Hsu, W.H., Ray, S.R., and Wilkins, D.C. 2000. A Multistrategy Approach to Classifier Learning from Time Series. Machine Learning, 38(1-2):213–236. Norwell, MA: Kluwer Academic Publishers.

  Google Scholar 

• Hsu, W. and Welge, M. to appear. Activities of the Prognostics Working Group. NCSA Technical Report.

• John, G., Kohavi, R., and Pfleger, K. 1994. Irrelevant features and the subset selection problem. In Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ. Morgan-Kaufmann, Los Altos, CA, pp. 121–129.

  Google Scholar 

• Jonske, J. 1999. Personal communication. Unpublished.

• Kohavi, R., Becker, B., and Sommerfield, D. 1997. Improving simple bayes. Presented at the European Conference on Machine Learning (ECML-97).

• Kohonen, T., Hynninen, J., Kangas, J., and Laaksonen, J. 1996. SOM-PAK: The Self-Organizing Map Program Package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02150 Espoo, Finland.

• Kohavi, R. and John, G.H. 1997. Wrappers for feature subset selection. Artificial Intelligence, Special Issue on Relevance, 97(1/2):273–324.

  Google Scholar 

• Kohonen, T. 1990. The self-organizing map. Proceedings of the IEEE, 78:1464–1480.

  Google Scholar 

• Koza, J. 1992. Genetic Programming: On the Programming of Computers by Natural Selection. Cambridge, MA: MIT Press.

  Google Scholar 

• Kononenko, I. 1994. Estimating attributes: Analysis and extensions of relief. In Proceedings of the European Conference on Machine Learning, F. Bergadano and L. De Raedt (Eds.).

• Kohavi, R. 1995. Wrappers for Performance Enhancement and Oblivious Decision Graphs. PhD Thesis, Department of Computer Science, Stanford University.

• Kohavi, R. 1998. MineSet v2.6, Silicon Graphics Incorporated, CA.

• Kira, K. and Rendell, L.A. 1992. The feature selection problem: Traditional methods and a new algorithm. In Proceedings of the National Conference on Artificial Intelligence (AAAI-92), San Jose, CA. Cambridge, MA: MIT Press, pp. 129–134.

  Google Scholar 

• Krishnamurthy, B. (Ed.) 1995. Practical Reusable UNIX Software. New York: John Wiley and Sons.

  Google Scholar 

• Kohavi, R. and Sommerfield, D. 1996. MLC++: Machine Learning Library in C++, Utilities v2.0. URL: http://www.sgi.com/Technology/mlc.

• Mitchell, T.M. 1997. Machine Learning. New York, NY: McGraw-Hill.

  Google Scholar 

• Neal, R.M. 1996. Bayesian Learning for Neural Networks. New York, NY: Springer-Verlag.

  Google Scholar 

• Princip, J. and Lefebvre, C. 1998. NeuroSolutions v3.02, NeuroDimension, Gainesville, FL. URL: http://www.nd.com.

• Porter, J. 1998. Personal communication. Unpublished.

• Quinlan, J.R. 1985. Induction of decision trees. Machine Learning, 1:81–106.

  Google Scholar 

• Quinlan, J.R. 1990. Learning logical definitions from relations. Machine Learning, 5(3):239–266.

  Google Scholar 

• Russell, S. and Norvig, P. 1995. Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall.

  Google Scholar 

• Raymer, M., Punch, W., Goodman, E., Sanschagrin, P., and Kuhn, L. 1997. Simultaneous feature extraction and selection using a masking genetic algorithm. In Proceedings of the 7th International Conference on Genetic Algorithms, San Francisco, CA, pp. 561–567.

• Sarle, W.S. (Ed.). Neural Network FAQ, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ.html

• Sterling, T.L., Salmon, J., Becker, D.J., and Savarese, D.F. 1999. How to Build a Beowulf: A Guide to the Implementation and Application of PC Clusters. Cambridge, MA: MIT Press.

  Google Scholar 

Download references