2015 (1)
Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science. Fawcett, T. Big Data, 3(4): 249 -266. 2015.
Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science [pdf]Paper   Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science [link]Website   bibtex   abstract
  2013 (2)
Data Science and its Relationship to Big Data and Data-Driven Decision Making. Provost, F.; and Fawcett, T. Big Data, 1(1): 51-59. 3 2013.
Data Science and its Relationship to Big Data and Data-Driven Decision Making [pdf]Paper   Data Science and its Relationship to Big Data and Data-Driven Decision Making [link]Website   bibtex   abstract
Data Science for Business. Provost, F.; and Fawcett, T. O'Reilly Media LLC, Second edition, 2013.
Data Science for Business [link]Website   bibtex   buy
  2008 (2)
Data Mining with Cellular Automata. Fawcett, T. SigKDD Explorations, 10(1): 32--39. 7 2008.
Data Mining with Cellular Automata [pdf]Paper   Data Mining with Cellular Automata [pdf]Website   bibtex   abstract
PRIE: A system for generating rulelists to maximize ROC performance. Fawcett, T. Data Mining and Knowledge Discovery, 17(2): 207-224. 2 2008.
PRIE: A system for generating rulelists to maximize ROC performance [pdf]Paper   PRIE: A system for generating rulelists to maximize ROC performance [link]Website   bibtex   abstract
  2007 (1)
PAV and the ROC convex hull. Fawcett, T.; and Niculescu-Mizil, A. Machine Learning, 68(1): 97-106. 2007.
PAV and the ROC convex hull [pdf]Paper   PAV and the ROC convex hull [link]Website   bibtex   abstract
  2006 (2)
An introduction to ROC analysis. Fawcett, T. Pattern Recognition Letters, 27(8): 861-874. 6 2006.
An introduction to ROC analysis [pdf]Paper   An introduction to ROC analysis [link]Website   bibtex   abstract
ROC graphs with instance-varying costs. Fawcett, T. Pattern Recognition Letters, 27(8): 882-891. 6 2006.
ROC graphs with instance-varying costs [pdf]Paper   ROC graphs with instance-varying costs [link]Website   bibtex   abstract
  2005 (2)
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Fawcett, T.; and Flach, P., A. Machine Learning, 58(1): 33-38. 1 2005.
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions [pdf]Paper   A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions [link]Website   bibtex   abstract
ROC graphs: Notes and practical considerations for researchers. Fawcett, T. Technical Report HP Laboratories, 2005.
ROC graphs: Notes and practical considerations for researchers [pdf]Paper   ROC graphs: Notes and practical considerations for researchers [link]Website   bibtex   abstract
  2004 (3)
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Lavrač, N.; Motoda, H.; Fawcett, T.; Holte, R.; Langley, P.; and Adriaans, P. Machine Learning, 57(1/2): 13-34. 10 2004.
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving [pdf]Paper   Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving [link]Website   bibtex
Editorial: Data Mining Lessons Learned. Lavrač, N.; Motoda, H.; and Fawcett, T. Machine Learning, 57(1/2): 5-11. 10 2004.
Editorial: Data Mining Lessons Learned [pdf]Paper   Editorial: Data Mining Lessons Learned [link]Website   bibtex
Editorial: Data Mining Lessons Learned. Lavrač, N.; Motoda, H.; and Fawcett, T. Machine Learning, 57(1/2): 5-11. 10 2004.
Editorial: Data Mining Lessons Learned [pdf]Paper   Editorial: Data Mining Lessons Learned [link]Website   bibtex
  2001 (2)
Case study: Fraud detection. Fawcett, T. H1.2: Case. Oxford University Press, 2001.
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Fraud Detection. Fawcett, T.; and Provost, F. F2: Fraud, pages 1-17. Oxford University Press, 2001.
Fraud Detection [pdf]Paper   bibtex
  1999 (1)
Activity monitoring: Noticing interesting changes in behavior. Fawcett, T.; and Provost, F. In Proceedings of KDD-99, volume 23, pages 33-46, 8 1999.
Activity monitoring: Noticing interesting changes in behavior [pdf]Paper   bibtex   abstract
  1997 (4)
Adaptive fraud detection. Fawcett, T.; and Provost, F. Data Mining and Knowledge Discovery, 1(3): 291-316. 1997.
Adaptive fraud detection [pdf]Paper   Adaptive fraud detection [pdf]Website   bibtex   abstract
Combining Data Mining and Machine Learning for Effective Fraud Detection. Fawcett, T.; and Provost, F. In AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, pages 14-19, 1997. AAAI Press
Combining Data Mining and Machine Learning for Effective Fraud Detection [pdf]Website   bibtex   abstract
AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management. AI Magazine, 19(2): 107-108. 1997.
AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management [pdf]Paper   AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management [pdf]Website   bibtex   abstract
Adaptive fraud detection. Fawcett, T.; and Provost, F. Data Mining and Knowledge Discovery, 1(3): 291-316. 1997.
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  1996 (2)
Knowledge based feature discovery for evaluation functions. Fawcett, T. Computational Intelligence, 12: 42-64. 1996.
Knowledge based feature discovery for evaluation functions [pdf]Paper   bibtex
Knowledge-Based Feature Discovery for Evaluation Functions. Fawcett, T. Computational Intelligence, 12(1): 42-64. 2 1996.
Knowledge-Based Feature Discovery for Evaluation Functions [pdf]Paper   Knowledge-Based Feature Discovery for Evaluation Functions [link]Website   bibtex   abstract
  1995 (2)
Case study: Adaptive fraud detection. Fawcett, T. pages 1-19. Oxford University Press, 1995.
Case study: Adaptive fraud detection [pdf]Paper   bibtex
Fraud Detection. Fawcett, T.; and Provost, F. pages 1-17. Oxford University Press, 1995.
Fraud Detection [pdf]Paper   bibtex
  1993 (1)
Feature Discovery for Problem Solving Systems. Fawcett, T. Ph.D. Thesis, 1993.
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  1992 (1)
Automatic feature generation for problem solving systems. Fawcett, T.; and Utgoff, P. In Proceedings of the Ninth International Conference on Machine Learning, pages 144-153, 1992. Morgan Kaufman
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  1991 (3)
Adaptive case-based reasoning. Callan, J.; and Fawcett, T. In Proceedings of the Third DARPA Case-Based Reasoning, pages 179-190, 1991. Morgan Kaufmann
Adaptive case-based reasoning [link]Website   bibtex
CABOT: An adaptive approach to case-based search. Callan, J.; Fawcett, T.; and Rissland, E. In IJCAI, pages 803-808, 1991.
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A hybrid method for feature generation. Fawcett, T.; and Utgoff, P. In Proceedings of the Eighth International Workshop on Machine Learning, pages 137-141, 1991. Morgan Kaufmann
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  1990 (2)
Generating useful approximations: A transformational model. Mostow, J.; and Fawcett, T. In 1990.
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Feature Discovery for Inductive Concept Learning 1 Introduction. Fawcett, T., E. Technical Report University of Massachusetts at Amherst, 1990.
Feature Discovery for Inductive Concept Learning 1 Introduction [link]Website   bibtex   abstract
  1988 (1)
A Framework for Integrating Heterogenous Learning Agents. Silver, B.; Vittal, J.; Frawley, W.; Iba, G.; Fawcett, T.; Dusseault, S.; and Doleac, J. pages 746-764. Springer-Verlag, 1988.
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  1987 (1)
Approximating intractable theories: A problem space model. Mostow, J.; and Fawcett, T. Technical Report Rutgers University, 1987.
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