| Glossary of terms R Kohavi, F Provost Machine Learning 30, 271-274, 1998 | 2800* | 1998 |
| Robust classification for imprecise environments F Provost, T Fawcett Machine learning 42 (3), 203-231, 2001 | 1406 | 2001 |
| Adaptive fraud detection T Fawcett, F Provost Data mining and knowledge discovery 1 (3), 291-316, 1997 | 1131 | 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 | 1026 | 2008 |
| Quality management on amazon mechanical turk PG Ipeirotis, F Provost, J Wang Proceedings of the ACM SIGKDD workshop on human computation, 64-67, 2010 | 1012 | 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 | 948 | 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 | 868 | 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 | 767 | 2013 |
| Network-based marketing: Identifying likely adopters via consumer networks S Hill, F Provost, C Volinsky Statistical Science 21 (2), 256-276, 2006 | 664 | 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 | 628 | 2007 |
| Tree induction for probability-based ranking F Provost, P Domingos Machine learning 52 (3), 199-215, 2003 | 576 | 2003 |
| Activity monitoring: Noticing interesting changes in behavior T Fawcett, F Provost Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 513 | 1999 |
| Machine learning from imbalanced data sets 101 F Provost Proceedings of the AAAI’2000 workshop on imbalanced data sets 68, 1-3, 2000 | 482 | 2000 |
| The effect of class distribution on classifier learning: an empirical study GM Weiss, F Provost | 474 | 2001 |
| Efficient progressive sampling F Provost, D Jensen, T Oates Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 415 | 1999 |
| A survey of methods for scaling up inductive algorithms F Provost, V Kolluri Data mining and knowledge discovery 3 (2), 131-169, 1999 | 387 | 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 | 386 | 2003 |
| Handling missing values when applying classification models M Saar-Tsechansky, F Provost Journal of machine learning research 8 (Jul), 1623-1657, 2007 | 342 | 2007 |
| A simple relational classifier SA Macskassy, F Provost NEW YORK UNIV NY STERN SCHOOL OF BUSINESS, 2003 | 334 | 2003 |
| Combining Data Mining and Machine Learning for Effective User Profiling. T Fawcett, FJ Provost KDD, 8-13, 1996 | 290 | 1996 |
