David Mease
Impact in
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
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- Manufacturing Process and Optimization
Papers in
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- Imbalanced Data Classification Techniques 3
- Machine Learning and Data Classification 2
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- Probabilistic and Robust Engineering Design 3
- Co-authors
- Abraham J. Wyner (4 shared papers)Andreas Buja (1 shared paper)Vijayan N. Nair (3 shared papers)Matthew Olson (1 shared paper)Justin Bleich (1 shared paper)Agus Sudjianto (1 shared paper)Miriam L. Matteson (1 shared paper)Daniel M. Russell (1 shared paper)
- Journals
- Journal of Machine Learning Research (3 papers)Technometrics (2 papers)The American Statistician (2 papers)The Library Quarterly (1 paper)SAE technical papers on CD-ROM/SAE technical paper series (1 paper)
- Partner nations
- United States
In The Last Decade
David Mease
15 papers receiving 623 citations
Peers
Comparison fields: 5 of 120
- Artificial Intelligence 298
- Industrial and Manufacturing Engineering 76
- Statistics and Probability 60
- Management of Technology and Innovation 35
- Health Information Management 22
Countries citing papers authored by David Mease
This map shows the geographic impact of David Mease'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 David Mease with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Mease more than expected).
Fields of papers citing papers by David Mease
This network shows the impact of papers produced by David Mease. 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 David Mease. The network helps show where David Mease may publish in the future.
Co-authors
The 14 scholars most cited alongside David Mease, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2007 | 187 | |
| 2 | 2017 | 137 | |
| 3 | Evidence Contrary to the Statistical View of Boosting | 2008 | 92 |
| 4 | 2004 | 81 | |
| 5 | 2003 | 42 | |
| 6 | 2009 | 27 | |
| 7 | 2011 | 27 | |
| 8 | 2014 | 20 | |
| 9 | Unique Optimal Partitions of Distributions and Connections to Hazard Rates and Stochastic Ordering | 2006 | 14 |
| 10 | 2006 | 7 | |
| 11 | 2013 | 7 | |
| 12 | Evidence Contrary to the Statistical View of Boosting: A Rejoinder to Responses | 2008 | 6 |
| 13 | 2003 | 3 | |
| 14 | 2011 | 1 | |
| 15 | 2004 | 1 |
About David Mease
David Mease is a scholar working on Artificial Intelligence, Statistics, Probability and Uncertainty, Information Systems, Statistics and Probability and Control and Systems Engineering, having authored 15 papers that have together received 652 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (3 papers), Probabilistic and Robust Engineering Design (3 papers), Information Retrieval and Search Behavior (3 papers), Advanced Statistical Methods and Models (2 papers), Expert finding and Q&A systems (2 papers), Machine Learning and Data Classification (2 papers), Decision-Making and Behavioral Economics (1 paper) and Reliability and Maintenance Optimization (1 paper). The work is most often cited by research in Artificial Intelligence (298 citations), Industrial and Manufacturing Engineering (76 citations), Statistics and Probability (60 citations), Management of Technology and Innovation (35 citations) and Health Information Management (22 citations). David Mease has collaborated with scholars based in United States. Frequent co-authors include Abraham J. Wyner, Andreas Buja, Vijayan N. Nair, Matthew Olson, Justin Bleich, Agus Sudjianto, Miriam L. Matteson, Daniel M. Russell, Neema Moraveji and Jacob Bien. Their work appears in journals such as Journal of Machine Learning Research, Technometrics, The American Statistician, The Library Quarterly and SAE technical papers on CD-ROM/SAE technical paper series.
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.