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2010 – 2019
- 2015
[j18]Tom Fawcett
:
Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science. Big Data 3(4): 249-266 (2015)- 2014
- [j17]Foster J. Provost, Tom Fawcett:
Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making". Big Data 2(1): 1 (2014) - 2013
- [j16]Foster J. Provost, Tom Fawcett:
Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data 1(1): 51-59 (2013) - 2011
- [j15]David Martens, Bart Baesens, Tom Fawcett:
Editorial survey: swarm intelligence for data mining. Mach. Learn. 82(1): 1-42 (2011)
2000 – 2009
- 2008
- [j14]Tom Fawcett:
PRIE: a system for generating rulelists to maximize ROC performance. Data Min. Knowl. Discov. 17(2): 207-224 (2008) - [j13]Tom Fawcett:
Data mining with cellular automata. SIGKDD Explor. 10(1): 32-39 (2008) - 2007
- [j12]Tom Fawcett, Alexandru Niculescu-Mizil:
PAV and the ROC convex hull. Mach. Learn. 68(1): 97-106 (2007) - 2006
- [j11]Tom Fawcett:
An introduction to ROC analysis. Pattern Recognit. Lett. 27(8): 861-874 (2006) - [j10]Tom Fawcett:
ROC graphs with instance-varying costs. Pattern Recognit. Lett. 27(8): 882-891 (2006) - 2005
- [j9]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. Mach. Learn. 58(1): 33-38 (2005) - 2004
- [j8]Nada Lavrac, Hiroshi Motoda, Tom Fawcett:
Editorial: Data Mining Lessons Learned. Mach. Learn. 57(1-2): 5-11 (2004) - [j7]Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans:
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Mach. Learn. 57(1-2): 13-34 (2004) - [i2]Tom Fawcett:
"In vivo" spam filtering: A challenge problem for data mining. CoRR cs.AI/0405007 (2004) - 2003
- [j6]Tom Fawcett:
"In vivo" spam filtering: a challenge problem for KDD. SIGKDD Explor. 5(2): 140-148 (2003) - [e2]Tom Fawcett, Nina Mishra:
Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA. AAAI Press 2003, ISBN 1-57735-189-4 [contents] - 2001
- [j5]Foster J. Provost, Tom Fawcett:
Robust Classification for Imprecise Environments. Mach. Learn. 42(3): 203-231 (2001) - [c11]Tom Fawcett:
Using Rule Sets to Maximize ROC Performance. ICDM 2001: 131-138 - [e1]Tom Fawcett:
Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26-29, 2001. ACM 2001 [contents] - 2000
- [i1]Foster J. Provost, Tom Fawcett:
Robust Classification for Imprecise Environments. CoRR cs.LG/0009007 (2000)
1990 – 1999
- 1999
- [c10]Tom Fawcett, Foster J. Provost:
Activity Monitoring: Noticing Interesting Changes in Behavior. KDD 1999: 53-62 - 1998
- [j4]Tom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo:
AI Approaches to Fraud Detection and Risk Management. AI Mag. 19(2): 107-108 (1998) - [c9]Foster J. Provost, Tom Fawcett:
Robust Classification Systems for Imprecise Environments. AAAI/IAAI 1998: 706-713 - [c8]Foster J. Provost, Tom Fawcett, Ron Kohavi:
The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML 1998: 445-453 - 1997
- [j3]Tom Fawcett, Foster J. Provost:
Adaptive Fraud Detection. Data Min. Knowl. Discov. 1(3): 291-316 (1997) - [c7]Foster J. Provost, Tom Fawcett:
Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. KDD 1997: 43-48 - 1996
- [j2]Tom Fawcett:
Knowledge-Based Feature Discovery for Evaluation Functions. Comput. Intell. 12: 42-64 (1996) - [c6]Tom Fawcett, Foster J. Provost:
Combining Data Mining and Machine Learning for Effective User Profiling. KDD 1996: 8-13 - 1992
- [j1]John Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac:
Intelligent and Cooperative Information Systems Meet Machine Learning. Int. J. Cooperative Inf. Syst. 1(2): 347-362 (1992) - [c5]Tom Fawcett, Paul E. Utgoff:
Automatic Feature Generation for Problem Solving Systems. ML 1992: 144-153 - 1991
- [c4]Tom Fawcett, Paul E. Utgoff:
A Hybrid Method for Feature Generation. ML 1991: 137-141 - [c3]John Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac:
A Framework for Cooperative Adaptable Information Systems. The Next Generation of Information Systems 1991: 169-184 - [c2]James P. Callan, Tom Fawcett, Edwina L. Rissland:
CABOT: An Adaptive Approach to Case-Based Search. IJCAI 1991: 803-809 - [d1]Tom Fawcett:
Othello Domain Theory. UCI Machine Learning Repository, 1991
1980 – 1989
- 1989
- [c1]Tom Fawcett:
Learning from Plausible Explanations. ML 1989: 37-39
Coauthor Index
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