| Glossary of terms R Kohavi, F Provost Machine Learning 30, 271-274, 1998 | 2652* | 1998 |
| Robust classification for imprecise environments F Provost, T Fawcett Machine learning 42 (3), 203-231, 2001 | 1348 | 2001 |
| Adaptive fraud detection T Fawcett, F Provost Data mining and knowledge discovery 1 (3), 291-316, 1997 | 1069 | 1997 |
| Get another label? improving data quality and data mining using multiple, noisy labelers VS Sheng, F Provost, PG Ipeirotis Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 959 | 2008 |
| Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. FJ Provost, T Fawcett KDD 97, 43-48, 1997 | 948 | 1997 |
| Quality management on amazon mechanical turk PG Ipeirotis, F Provost, J Wang Proceedings of the ACM SIGKDD workshop on human computation, 64-67, 2010 | 943 | 2010 |
| Learning when training data are costly: The effect of class distribution on tree induction GM Weiss, F Provost Journal of artificial intelligence research 19, 315-354, 2003 | 906 | 2003 |
| Data science and its relationship to big data and data-driven decision making F Provost, T Fawcett Big data 1 (1), 51-59, 2013 | 692 | 2013 |
| Data Science for Business: What you need to know about data mining and data-analytic thinking F Provost, T Fawcett " O'Reilly Media, Inc.", 2013 | 651 | 2013 |
| Network-based marketing: Identifying likely adopters via consumer networks S Hill, F Provost, C Volinsky Statistical Science 21 (2), 256-276, 2006 | 619 | 2006 |
| Classification in networked data: A toolkit and a univariate case study SA Macskassy, F Provost Journal of machine learning research 8 (May), 935-983, 2007 | 610 | 2007 |
| Tree induction for probability-based ranking F Provost, P Domingos Machine learning 52 (3), 199-215, 2003 | 555 | 2003 |
| Activity Monitoring: Noticing Interesting Changes in Behavior. T Fawcett, FJ Provost KDD 99, 53-62, 1999 | 495 | 1999 |
| Machine learning from imbalanced data sets 101 F Provost Proceedings of the AAAI’2000 workshop on imbalanced data sets 68 (2000), 1-3, 2000 | 432 | 2000 |
| Efficient progressive sampling F Provost, D Jensen, T Oates Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 396 | 1999 |
| Tree induction vs. logistic regression: A learning-curve analysis C Perlich, F Provost, JS Simonoff Journal of Machine Learning Research 4 (Jun), 211-255, 2003 | 372 | 2003 |
| The effect of class distribution on classifier learning: an empirical study GM Weiss, F Provost | 344 | 2001 |
| A survey of methods for scaling up inductive algorithms F Provost, V Kolluri Data mining and knowledge discovery 3 (2), 131-169, 1999 | 337 | 1999 |
| A simple relational classifier SA Macskassy, F Provost NEW YORK UNIV NY STERN SCHOOL OF BUSINESS, 2003 | 323 | 2003 |
| Handling missing values when applying classification models M Saar-Tsechansky, F Provost Journal of machine learning research 8 (Jul), 1623-1657, 2007 | 311 | 2007 |
