Tom Fawcett - researchr alias

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Viewing Publication 1 - 29 from 29

2015

Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data ScienceTom Fawcett. bigdata, 3(4):249-266, 2015. [doi]

2014

Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making"Foster J. Provost, Tom Fawcett. bigdata, 2(1):1, 2014. [doi]

2013

Data Science and its Relationship to Big Data and Data-Driven Decision MakingFoster J. Provost, Tom Fawcett. bigdata, 1(1):51-59, 2013. [doi]

2011

Editorial survey: swarm intelligence for data miningDavid Martens, Bart Baesens, Tom Fawcett. ml, 82(1):1-42, 2011. [doi]

2008

PRIE: a system for generating rulelists to maximize ROC performanceTom Fawcett. datamine, 17(2):207-224, 2008. [doi]

Data mining with cellular automataTom Fawcett. sigkdd, 10(1):32-39, 2008. [doi]

2007

PAV and the ROC convex hullTom Fawcett, Alexandru Niculescu-Mizil. ml, 68(1):97-106, 2007. [doi]

2006

An introduction to ROC analysisTom Fawcett. prl, 27(8):861-874, 2006. [doi]

ROC graphs with instance-varying costsTom Fawcett. prl, 27(8):882-891, 2006. [doi]

2005

A Response to Webb and Ting s ::::On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions::::Tom Fawcett, Peter A. Flach. ml, 58(1):33-38, 2005. [doi]

2004

Editorial: Data Mining Lessons LearnedNada Lavrac, Hiroshi Motoda, Tom Fawcett. ml, 57(1-2):5-11, 2004. [doi]

Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem SolvingNada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans. ml, 57(1-2):13-34, 2004. [doi]

2003

In vivo spam filtering: a challenge problem for KDDTom Fawcett. sigkdd, 5(2):140-148, 2003. [doi]

2001

Using Rule Sets to Maximize ROC PerformanceTom Fawcett. icdm 2001: 131-138 [doi]

Robust Classification for Imprecise EnvironmentsFoster J. Provost, Tom Fawcett. ml, 42(3):203-231, 2001.

1999

Activity Monitoring: Noticing Interesting Changes in BehaviorTom Fawcett, Foster J. Provost. kdd 1999: 53-62 [doi]

1998

AI Approaches to Fraud Detection and Risk ManagementTom Fawcett, Ira J. Haimowitz, Foster J. Provost, Salvatore J. Stolfo. aim, 19(2):107-108, 1998.

The Case against Accuracy Estimation for Comparing Induction AlgorithmsFoster J. Provost, Tom Fawcett, Ron Kohavi. icml 1998: 445-453

Robust Classification Systems for Imprecise EnvironmentsFoster J. Provost, Tom Fawcett. AAAI 1998: 706-713

1997

Adaptive Fraud DetectionTom Fawcett, Foster J. Provost. datamine, 1(3):291-316, 1997.

Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost DistributionsFoster J. Provost, Tom Fawcett. kdd 1997: 43-48

1996

Knowledge-Based Feature Discovery for Evaluation FunctionsTom Fawcett. ci, 12:42-64, 1996.

Combining Data Mining and Machine Learning for Effective User ProfilingTom Fawcett, Foster J. Provost. kdd 1996: 8-13

1992

Automatic Feature Generation for Problem Solving SystemsTom Fawcett, Paul E. Utgoff. icml 1992: 144-153

Intelligent and Cooperative Information Systems Meet Machine LearningJohn Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac. ijcis, 2(2):347-362, 1992.

1991

CABOT: An Adaptive Approach to Case-Based SearchJames P. Callan, Tom Fawcett, Edwina L. Rissland. IJCAI 1991: 803-809

A Hybrid Method for Feature GenerationTom Fawcett, Paul E. Utgoff. icml 1991: 137-141

A Framework for Cooperative Adaptable Information SystemsJohn Vittal, Bernard Silver, William J. Frawley, Glenn A. Iba, Tom Fawcett, Susan Dusseault, John Doleac. IJCAI 1992: 169-184

1989

Learning from Plausible ExplanationsTom Fawcett. icml 1989: 37-39

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