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Tom Fawcett: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Tom Fawcett, Foster J. Provost
    Adaptive Fraud Detection. [Citation Graph (1, 0)][DBLP]
    Data Min. Knowl. Discov., 1997, v:1, n:3, pp:291-316 [Journal]
  2. Foster J. Provost, Tom Fawcett
    Robust Classification Systems for Imprecise Environments. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:706-713 [Conf]
  3. Tom Fawcett
    Using Rule Sets to Maximize ROC Performance. [Citation Graph (0, 0)][DBLP]
    ICDM, 2001, pp:131-138 [Conf]
  4. Tom Fawcett
    Learning from Plausible Explanations. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:37-39 [Conf]
  5. Tom Fawcett, Paul E. Utgoff
    A Hybrid Method for Feature Generation. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:137-141 [Conf]
  6. Tom Fawcett, Paul E. Utgoff
    Automatic Feature Generation for Problem Solving Systems. [Citation Graph (0, 0)][DBLP]
    ML, 1992, pp:144-153 [Conf]
  7. Foster J. Provost, Tom Fawcett, Ron Kohavi
    The Case against Accuracy Estimation for Comparing Induction Algorithms. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:445-453 [Conf]
  8. James P. Callan, Tom Fawcett, Edwina L. Rissland
    CABOT: An Adaptive Approach to Case-Based Search. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1991, pp:803-809 [Conf]
  9. John Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac
    A Framework for Cooperative Adaptable Information Systems. [Citation Graph (0, 0)][DBLP]
    The Next Generation of Information Systems, 1991, pp:169-184 [Conf]
  10. Tom Fawcett, Foster J. Provost
    Combining Data Mining and Machine Learning for Effective User Profiling. [Citation Graph (0, 0)][DBLP]
    KDD, 1996, pp:8-13 [Conf]
  11. Tom Fawcett, Foster J. Provost
    Activity Monitoring: Noticing Interesting Changes in Behavior. [Citation Graph (0, 0)][DBLP]
    KDD, 1999, pp:53-62 [Conf]
  12. Foster J. Provost, Tom Fawcett
    Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. [Citation Graph (0, 0)][DBLP]
    KDD, 1997, pp:43-48 [Conf]
  13. Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo
    AI Approaches to Fraud Detection and Risk Management. [Citation Graph (0, 0)][DBLP]
    AI Magazine, 1998, v:19, n:2, pp:107-108 [Journal]
  14. Tom Fawcett
    Knowledge-Based Feature Discovery for Evaluation Functions. [Citation Graph (0, 0)][DBLP]
    Computational Intelligence, 1996, v:12, n:, pp:42-64 [Journal]
  15. Tom Fawcett
    "In vivo" spam filtering: A challenge problem for data mining [Citation Graph (0, 0)][DBLP]
    CoRR, 2004, v:0, n:, pp:- [Journal]
  16. Foster J. Provost, Tom Fawcett
    Robust Classification for Imprecise Environments [Citation Graph (0, 0)][DBLP]
    CoRR, 2000, v:0, n:, pp:- [Journal]
  17. John Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac
    Intelligent and Cooperative Information Systems Meet Machine Learning. [Citation Graph (0, 0)][DBLP]
    Int. J. Cooperative Inf. Syst., 1992, v:2, n:2, pp:347-362 [Journal]
  18. Tom Fawcett, Peter A. Flach
    A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2005, v:58, n:1, pp:33-38 [Journal]
  19. Nada Lavrac, Hiroshi Motoda, Tom Fawcett
    Editorial: Data Mining Lessons Learned. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:5-11 [Journal]
  20. Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans
    Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:57, n:1-2, pp:13-34 [Journal]
  21. Foster J. Provost, Tom Fawcett
    Robust Classification for Imprecise Environments. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:42, n:3, pp:203-231 [Journal]
  22. Tom Fawcett
    An introduction to ROC analysis. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2006, v:27, n:8, pp:861-874 [Journal]
  23. Tom Fawcett
    ROC graphs with instance-varying costs. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2006, v:27, n:8, pp:882-891 [Journal]
  24. Tom Fawcett
    "In vivo" spam filtering: a challenge problem for KDD. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:140-148 [Journal]
  25. Tom Fawcett, Alexandru Niculescu-Mizil
    PAV and the ROC convex hull. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2007, v:68, n:1, pp:97-106 [Journal]

  26. PRIE: a system for generating rulelists to maximize ROC performance. [Citation Graph (, )][DBLP]


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