Tom Fawcett

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AI Approaches to Fraud Detection and Risk Management

Salvatore J. Stolfo, Tom Fawcett, Ira J. Haimowitz, 1998, AI Mag..

Industry: adaptive fraud detection

Tom Fawcett, 2002 .

Data science for business

Tom Fawcett, Foster Provost, Tom Fawcett, 2013 .

A Hybrid Method for Feature Generation

Paul E. Utgoff, Tom Fawcett, P. Utgoff, 1991, ML.

"In vivo" spam filtering: a challenge problem for KDD

Tom Fawcett, Tom Fawcett, 2003, SKDD.

The National Shipbuilding Research Program. Application of Industrial Engineering Techniques to Reduce Workers' Compensation and Environmental Costs - Deliverable G

Tom Fawcett, Freddie Hogan, 1999 .

ROC Graphs: Notes and Practical Considerations for Data Mining Researchers

Tom Fawcett, Tom Fawcett, 2003 .

Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving

Nada Lavrac, Pat Langley, Hiroshi Motoda, 2004, Machine Learning.

PAV and the ROC convex hull

Tom Fawcett, Alexandru Niculescu-Mizil, Tom Fawcett, 2007, Machine Learning.

An introduction to ROC analysis

Tom Fawcett, Tom Fawcett, 2006, Pattern Recognit. Lett..

ROC graphs with instance-varying costs

Tom Fawcett, Tom Fawcett, 2006, Pattern Recognit. Lett..

Combining Data Mining and Machine Learning for Effective Fraud Detection

Tom Fawcett, Foster Provost, Tom Fawcett, 1997 .

Editorial survey: swarm intelligence for data mining

Bart Baesens, Tom Fawcett, David Martens, 2010, Machine Learning.

PRIE: a system for generating rulelists to maximize ROC performance

Tom Fawcett, 2008, Data Mining and Knowledge Discovery.

KNOWLEDGE‐BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Tom Fawcett, Tom Fawcett, 1996, Comput. Intell..

Automatic Feature Generation for Problem Solving Systems

Paul E. Utgoff, Tom Fawcett, P. Utgoff, 1992, ML.

Proceedings, Twentieth International Conference on Machine Learning

Tom Fawcett, Nina Mishra, Tom Fawcett, 2003 .

A Framework for Cooperative Adaptable Information Systems

Tom Fawcett, Bernard Silver, Glenn A. Iba, 1991, The Next Generation of Information Systems.

Robust Classification Systems for Imprecise Environments

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1998, AAAI/IAAI.

Tutorial notes of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining

Tom Fawcett, 2001, KDD 2001.

The origin of the psychologically orientated knowledge base within strength and conditioning

Tom Fawcett, Paul Comfort, Jon N. Radcliffe, 2014 .

Data Science and its Relationship to Big Data and Data-Driven Decision Making

Tom Fawcett, Foster Provost, Tom Fawcett, 2013, Big Data.

"In vivo" spam filtering: A challenge problem for data mining

Tom Fawcett, 2004, ArXiv.

Robust Classification for Imprecise Environments

Tom Fawcett, Foster J. Provost, Tom Fawcett, 2000, Machine Learning.

Combining Data Mining and Machine Learning for Effective User Profiling

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1996, KDD.

The Case against Accuracy Estimation for Comparing Induction Algorithms

Ron Kohavi, Tom Fawcett, Foster J. Provost, 1998, ICML.

The effect of manipulations of perceived goal difficulty on the execution of the hang power clean

Tom Fawcett, Paul Comfort, Jon N. Radcliffe, 2014 .

A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions

Peter A. Flach, Tom Fawcett, Tom Fawcett, 2005, Machine Learning.

Learning from Plausible Explanations

Tom Fawcett, 1989, ML.

Intelligent and Cooperative Information Systems Meet Machine Learning

Tom Fawcett, Bernard Silver, Glenn A. Iba, 1992, Int. J. Cooperative Inf. Syst..

Activity monitoring: noticing interesting changes in behavior

Tom Fawcett, Foster Provost, Tom Fawcett, 1999, KDD '99.

Authors' Response to Gong's, "Comment on Data Science and its Relationship to Big Data and Data-Driven Decision Making"

Tom Fawcett, Foster Provost, Foster Provost, 2014, Big Data.

Fraud detection

Tom Fawcett, Foster Provost, Tom Fawcett, 2002 .

AAAI-98 Workshops: Reports of the Workshops Held at the Fifteenth National Conference on Artificial Intelligence in Madison, Wisconsin

David W. Aha, Brian Logan, Tom Fawcett, 1999 .

Editorial: Data Mining Lessons Learned

Nada Lavrac, Hiroshi Motoda, Tom Fawcett, 2004, Machine Learning.

A framework for integrating heterogeneous learning agents

Tom Fawcett, Bernard Silver, Glenn A. Iba, 1993 .

Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21-24, 2003, Washington, DC, USA

Tom Fawcett, Nina Mishra, 2003 .

Data mining with cellular automata

Tom Fawcett, Tom Fawcett, 2008, SKDD.

Adaptive Fraud Detection

Tom Fawcett, Foster Provost, Tom Fawcett, 1997, Data Mining and Knowledge Discovery.

Data science for business

Tom Fawcett, Foster Provost, Tom Fawcett, 2013 .

Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science

Tom Fawcett, 2015, Big Data.

Analysis and Visualization of Classifier Performance with Nonuniform Class and Cost Distributions

Tom Fawcett, Foster Provost, Tom Fawcett, 1997 .

Using rule sets to maximize ROC performance

Tom Fawcett, Tom Fawcett, 2001, Proceedings 2001 IEEE International Conference on Data Mining.

Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions

Tom Fawcett, Foster J. Provost, Tom Fawcett, 1997, KDD.

CABOT: An Adaptive Approach to Case-Based Search

Edwina L. Rissland, Tom Fawcett, James P. Callan, 1991, IJCAI.

Technical Note: PAV and the ROC Convex Hull

Tom Fawcett, Alexandru Niculescu-Mizil, Tom Fawcett, 2007 .