Cullen Schaffer
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Neural Networks and Applications
- Machine Learning and Algorithms
- Evolutionary Algorithms and Applications
- Information Systems top 5%
- Data Mining Algorithms and Applications
Papers in
-
- Imbalanced Data Classification Techniques 4
- Machine Learning and Data Classification 4
- Advanced Text Analysis Techniques 3
- Machine Learning and Algorithms 2
- Neural Networks and Applications 2
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- Data Mining Algorithms and Applications 3
- Co-authors
- Casimir A. Kulikowski (1 shared paper)
- Journals
- Machine Learning (5 papers)Planta (1 paper)Medical Entomology and Zoology (1 paper)National Conference on Artificial Intelligence (2 papers)
- Partner nations
- United States
In The Last Decade
Cullen Schaffer
10 papers receiving 723 citations
Peers
Comparison fields: 5 of 142
- Artificial Intelligence 383
- Information Systems 137
- Computational Mathematics 3
- Signal Processing 46
- Computational Theory and Mathematics 64
Countries citing papers authored by Cullen Schaffer
This map shows the geographic impact of Cullen Schaffer'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 Cullen Schaffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cullen Schaffer more than expected).
Fields of papers citing papers by Cullen Schaffer
This network shows the impact of papers produced by Cullen Schaffer. 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 Cullen Schaffer. The network helps show where Cullen Schaffer may publish in the future.
Co-authors
The 1 scholars most cited alongside Cullen Schaffer, 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 | 1993 | 324 | |
| 2 | 1993 | 152 | |
| 3 | 1993 | 125 | |
| 4 | 1993 | 121 | |
| 5 | Sparse data and the effect of overfitting avoidance in decision tree induction | 1992 | 20 |
| 6 | Principles of computer science | 1988 | 14 |
| 7 | A proven domain-independent scientific function-finding algorithm | 1990 | 9 |
| 8 | 1993 | 5 | |
| 9 | 1990 | 4 | |
| 10 | On Evaluation of Domain-Independent Scientific Function-Finding Systems. | 1991 | 1 |
About Cullen Schaffer
Cullen Schaffer is a scholar working on Artificial Intelligence, Information Systems, Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 10 papers that have together received 775 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (4 papers), Machine Learning and Data Classification (4 papers), Advanced Text Analysis Techniques (3 papers), Data Mining Algorithms and Applications (3 papers), Machine Learning and Algorithms (2 papers), Biomedical Text Mining and Ontologies (2 papers), Data Visualization and Analytics (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (383 citations), Information Systems (137 citations), Computational Mathematics (3 citations), Signal Processing (46 citations) and Computational Theory and Mathematics (64 citations). Cullen Schaffer has collaborated with scholars based in United States. Frequent co-authors include Casimir A. Kulikowski. Their work appears in journals such as Machine Learning, Planta, Medical Entomology and Zoology and National Conference on Artificial Intelligence.
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.