| Data science and its relationship to big data and data-driven decision making F Provost, T Fawcett Big data 1 (1), 51-59, 2013 | 1772 | 2013 |
| Glossary of terms R Kohavi, F Provost Machine Learning 30, 271-274, 1998 | 1752 | 1998 |
| Robust classification for imprecise environments F Provost, T Fawcett Machine learning 42, 203-231, 2001 | 1675 | 2001 |
| The Case Against Accuracy Estimation for Comparing Induction Algorithms F Provost, T Fawcett, R Kohavi Proceedings of ICML-98, 445-453, 1998 | 1599 | 1998 |
| 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 | 1386 | 2013 |
| 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 | 1362 | 2008 |
| Adaptive fraud detection T Fawcett, F Provost Data mining and knowledge discovery 1 (3), 291-316, 1997 | 1340 | 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 | 1276 | 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 | 1190 | 2003 |
| Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions. F Provost, T Fawcett In Proceedings of the Third International Conference on Knowledge Discovery …, 1997 | 1131 | 1997 |
| Network-based marketing: Identifying likely adopters via consumer networks S Hill, F Provost, C Volinsky | 840 | 2006 |
| Machine learning from imbalanced data sets 101 F Provost Proceedings of the AAAI’2000 workshop on imbalanced data sets 68 (2000), 1-3, 2000 | 738 | 2000 |
| Classification in networked data: A toolkit and a univariate case study. SA Macskassy, F Provost Journal of machine learning research 8 (5), 2007 | 709 | 2007 |
| Tree induction for probability-based ranking F Provost, P Domingos Machine learning 52, 199-215, 2003 | 675 | 2003 |
| The effect of class distribution on classifier learning: an empirical study GM Weiss, F Provost Rutgers University, 2001 | 608 | 2001 |
| Activity monitoring: Noticing interesting changes in behavior T Fawcett, F Provost Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 589 | 1999 |
| Efficient progressive sampling F Provost, D Jensen, T Oates Proceedings of the fifth ACM SIGKDD international conference on Knowledge …, 1999 | 506 | 1999 |
| Tree induction vs. logistic regression: A learning-curve analysis C Perlich, F Provost, J Simonoff Journal of Machine Learning Research, 2003 | 498 | 2003 |
| Handling missing values when applying classification models M Saar-Tsechansky, F Provost Journal of Machine Learning Research, 2007 | 460 | 2007 |
| A survey of methods for scaling up inductive algorithms F Provost, V Kolluri Data mining and knowledge discovery 3, 131-169, 1999 | 401 | 1999 |
