Learning from Plausible Explanations. - Tom Fawcett (ML, 1989)

A Hybrid Method for Feature Generation. - Tom Fawcett; Paul E. Utgoff (ML, 1991)

CABOT: An Adaptive Approach to Case-Based Search. - James P. Callan; Tom Fawcett; Edwina L. Rissland (IJCAI, 1991)

A Framework for Cooperative Adaptable Information Systems. - John Vittal; Bernard Silver; William J. Frawley; Glenn A. Iba; Tom Fawcett; Susan Dusseault; John Doleac (The Next Generation of Information Systems, 1991)

Othello Domain Theory. - Tom Fawcett (UCI Machine Learning Repository, 1991)

Automatic Feature Generation for Problem Solving Systems. - Tom Fawcett; Paul E. Utgoff (ML, 1992)

Intelligent and Cooperative Information Systems Meet Machine Learning. - John Vittal; Bernard Silver; William J. Frawley; Glenn A. Iba; Tom Fawcett; Susan Dusseault; John Doleac (Int. J. Cooperative Inf. Syst., 1992)

Combining Data Mining and Machine Learning for Effective User Profiling. - Tom Fawcett; Foster J. Provost (KDD, 1996)

Knowledge-Based Feature Discovery for Evaluation Functions. - Tom Fawcett (Comput. Intell., 1996)

Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. - Foster J. Provost; Tom Fawcett (KDD, 1997)

Adaptive Fraud Detection. - Tom Fawcett; Foster J. Provost (Data Min. Knowl. Discov., 1997)

Robust Classification Systems for Imprecise Environments. - Foster J. Provost; Tom Fawcett (AAAI/IAAI, 1998)

The Case against Accuracy Estimation for Comparing Induction Algorithms. - Foster J. Provost; Tom Fawcett; Ron Kohavi (ICML, 1998)

AI Approaches to Fraud Detection and Risk Management. - Tom Fawcett; Ira J. Haimowitz; Foster J. Provost; Salvatore J. Stolfo (AI Mag., 1998)

Activity Monitoring: Noticing Interesting Changes in Behavior. - Tom Fawcett; Foster J. Provost (KDD, 1999)

Using Rule Sets to Maximize ROC Performance. - Tom Fawcett (ICDM, 2001)

Robust Classification for Imprecise Environments. - Foster J. Provost; Tom Fawcett (Mach. Learn., 2001)

"In vivo" spam filtering: a challenge problem for KDD. - Tom Fawcett (SIGKDD Explor., 2003)

"In vivo" spam filtering: A challenge problem for data mining - Tom Fawcett (CoRR, 2004)

Editorial: Data Mining Lessons Learned. - Nada Lavrac; Hiroshi Motoda; Tom Fawcett (Mach. Learn., 2004)

Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. - Nada Lavrac; Hiroshi Motoda; Tom Fawcett; Robert Holte; Pat Langley; Pieter W. Adriaans (Mach. Learn., 2004)

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 (Mach. Learn., 2005)

An introduction to ROC analysis. - Tom Fawcett (Pattern Recognit. Lett., 2006)

ROC graphs with instance-varying costs. - Tom Fawcett (Pattern Recognit. Lett., 2006)

PAV and the ROC convex hull. - Tom Fawcett; Alexandru Niculescu-Mizil (Mach. Learn., 2007)

PRIE: a system for generating rulelists to maximize ROC performance. - Tom Fawcett (Data Min. Knowl. Discov., 2008)

Data mining with cellular automata. - Tom Fawcett (SIGKDD Explor., 2008)

Editorial survey: swarm intelligence for data mining. - David Martens; Bart Baesens; Tom Fawcett (Mach. Learn., 2011)

Data Science and its Relationship to Big Data and Data-Driven Decision Making. - Foster J. Provost; Tom Fawcett (Big Data, 2013)

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 (Big Data, 2014)

Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science. - Tom Fawcett (Big Data, 2015)