Foster John Provost
Impact in
- Computer Science Applications top 0.1%
- Mobile Crowdsensing and Crowdsourcing
- Artificial Intelligence top 0.05%
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
- Machine Learning and Algorithms
Papers in
-
- Imbalanced Data Classification Techniques 39
- Machine Learning and Data Classification 35
- Machine Learning and Algorithms 29
- Anomaly Detection Techniques and Applications 12
- Bayesian Modeling and Causal Inference 11
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- Data Mining Algorithms and Applications 32
- Co-authors
- Tom E. Fawcett (15 shared papers)Panagiotis G. Ipeirotis (6 shared papers)Gary M. Weiss (3 shared papers)Victor S. Sheng (3 shared papers)Ron Kohavi (3 shared papers)Jing Wang (3 shared papers)Maytal Saar‐Tsechansky (9 shared papers)Sofus A. Macskassy (12 shared papers)
- Journals
- Machine Learning (12 papers)Big Data (9 papers)Information Systems Research (6 papers)Data Mining and Knowledge Discovery (6 papers)Journal of the Association for Information Systems (3 papers)
- Partner nations
- United StatesBelgiumHong Kong
In The Last Decade
Foster John Provost
141 papers receiving 12.4k citations
Foster John Provost's Hit Papers
Peers
Comparison fields: 5 of 208
- Computer Science Applications 1.5k
- Artificial Intelligence 8.0k
- Information Systems 3.3k
- Management Science and Operations Research 1.5k
- Management Information Systems 937
Countries citing papers authored by Foster John Provost
This map shows the geographic impact of Foster John Provost's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Foster John Provost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Foster John Provost more than expected).
Fields of papers citing papers by Foster John Provost
This network shows the impact of papers produced by Foster John Provost. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Foster John Provost. The network helps show where Foster John Provost may publish in the future.
Co-authors
The 25 scholars most cited alongside Foster John Provost, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 144 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Data Science and its Relationship to Big Data and Data-Driven Decision Making Hit paper breakdown → | 2013 | 1107 |
| 2 | Robust Classification for Imprecise Environments Hit paper breakdown → | 2001 | 979 |
| 3 | Get another label? improving data quality and data mining using multiple, noisy labelers Hit paper breakdown → | 2008 | 875 |
| 4 | The Case against Accuracy Estimation for Comparing Induction Algorithms Hit paper breakdown → | 1998 | 821 |
| 5 | Quality management on Amazon Mechanical Turk Hit paper breakdown → | 2010 | 762 |
| 6 | Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction Hit paper breakdown → | 2003 | 754 |
| 7 | Adaptive Fraud Detection Hit paper breakdown → | 1997 | 684 |
| 8 | Analysis and visualization of classifier performance: comparison under imprecise class and cost distributions Hit paper breakdown → | 1997 | 595 |
| 9 | Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Hit paper breakdown → | 2013 | 459 |
| 10 | 2003 | 436 | |
| 11 | 2007 | 343 | |
| 12 | 1999 | 318 | |
| 13 | 1999 | 281 | |
| 14 | 2001 | 276 | |
| 15 | Machine Learning from Imbalanced Data Sets 101 | 2008 | 273 |
| 16 | Handling Missing Values when Applying Classification Models | 2007 | 240 |
| 17 | 1999 | 223 | |
| 18 | 2014 | 201 | |
| 19 | Active Sampling for Class Probability Estimation and Ranking | 2004 | 149 |
| 20 | 2005 | 146 |
About Foster John Provost
Foster John Provost is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Marketing and Statistical and Nonlinear Physics, having authored 144 papers that have together received 13.4k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (39 papers), Machine Learning and Data Classification (35 papers), Data Mining Algorithms and Applications (32 papers), Machine Learning and Algorithms (29 papers), Anomaly Detection Techniques and Applications (12 papers), Consumer Market Behavior and Pricing (12 papers), Complex Network Analysis Techniques (12 papers) and Bayesian Modeling and Causal Inference (11 papers). The work is most often cited by research in Computer Science Applications (1.5k citations), Artificial Intelligence (8.0k citations), Information Systems (3.3k citations), Management Science and Operations Research (1.5k citations) and Management Information Systems (937 citations). Foster John Provost has collaborated with scholars based in United States, Belgium and Hong Kong. Frequent co-authors include Tom E. Fawcett, Panagiotis G. Ipeirotis, Gary M. Weiss, Victor S. Sheng, Ron Kohavi, Jing Wang, Maytal Saar‐Tsechansky, Sofus A. Macskassy, Pedro Domingos and David Martens. Their work appears in journals such as Machine Learning, Big Data, Information Systems Research, Data Mining and Knowledge Discovery and Journal of the Association for Information Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.