Kendrick Boyd
Impact in
- Artificial Intelligence top 10%
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Machine Learning and Data Classification
- Machine Learning in Healthcare
Papers in
-
- Privacy-Preserving Technologies in Data 2
- Cryptography and Data Security 2
- Machine Learning and Data Classification 1
- Machine Learning and Algorithms 1
- Bayesian Modeling and Causal Inference 1
- Co-authors
- C. David Page (2 shared papers)Kevin Hasegawa Eng (1 shared paper)Vı́tor Santos Costa (1 shared paper)Jesse J. Davis (1 shared paper)David L. Page (2 shared papers)Éric Lantz (2 shared papers)Jeremy C. Weiss (1 shared paper)David I. Page (1 shared paper)
- Journals
- Lecture notes in computer science (1 paper)PubMed (4 papers)
- Partner nations
- United StatesBelgiumPortugal
In The Last Decade
Kendrick Boyd
5 papers receiving 356 citations
Peers
Comparison fields: 5 of 113
- Artificial Intelligence 150
- Health Information Management 11
- Signal Processing 26
- Computer Vision and Pattern Recognition 48
- Computer Science Applications 10
Countries citing papers authored by Kendrick Boyd
This map shows the geographic impact of Kendrick Boyd'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 Kendrick Boyd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kendrick Boyd more than expected).
Fields of papers citing papers by Kendrick Boyd
This network shows the impact of papers produced by Kendrick Boyd. 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 Kendrick Boyd. The network helps show where Kendrick Boyd may publish in the future.
Co-authors
The 8 scholars most cited alongside Kendrick Boyd, 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 | 2013 | 269 | |
| 2 | Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. | 2012 | 48 |
| 3 | 2015 | 19 | |
| 4 | Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution. | 2015 | 19 |
| 5 | 2015 | 7 |
About Kendrick Boyd
Kendrick Boyd is a scholar working on Artificial Intelligence, Computer Science Applications, Statistics and Probability, Computational Theory and Mathematics and Information Systems, having authored 5 papers that have together received 362 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (2 papers), Cryptography and Data Security (2 papers), Machine Learning and Data Classification (1 paper), Rough Sets and Fuzzy Logic (1 paper), Machine Learning and Algorithms (1 paper), Advanced Causal Inference Techniques (1 paper), Statistical Methods and Inference (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Artificial Intelligence (150 citations), Health Information Management (11 citations), Signal Processing (26 citations), Computer Vision and Pattern Recognition (48 citations) and Computer Science Applications (10 citations). Kendrick Boyd has collaborated with scholars based in United States, Belgium and Portugal. Frequent co-authors include C. David Page, Kevin Hasegawa Eng, Vı́tor Santos Costa, Jesse J. Davis, David L. Page, Éric Lantz, Jeremy C. Weiss and David I. Page. Their work appears in journals such as Lecture notes in computer science and PubMed.
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.