Robert E. Banfield

846 citations
12 papers · 514 · h-index 8

Impact in

    • Machine Learning and Data Classification
    • Neural Networks and Applications
    • Imbalanced Data Classification Techniques
    • Anomaly Detection Techniques and Applications
    • Data Stream Mining Techniques
    • Evolutionary Algorithms and Applications
    • Face and Expression Recognition

Papers in

Robert E. Banfield

12 papers receiving 492 citations

Peers

Robert E. Banfield
Comparison fields: 5 of 96
  • Artificial Intelligence 323
  • Computer Vision and Pattern Recognition 104
  • Signal Processing 49
  • Health Information Management 19
  • Information Systems 88
Replace Xuewen Chen with:
Xuewen Chen United States
Takao Mohri Japan
Nadia Bolshakova Ireland
R. Barandela Mexico
Mark Junjie Li China
B. Venkatesh India
N. Ramaraj India
Cristiano Leite de Castro Brazil
David A. Cieslak United States
Guang-Tong Zhou China
Robert E. Banfield relative to Xuewen Chen United States Xuewen Chen's profile →
Citations per field
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Xuewen Chen · 1×
Citations per year

Countries citing papers authored by Robert E. Banfield

Since Specialization
Citations

This map shows the geographic impact of Robert E. Banfield'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 Robert E. Banfield with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert E. Banfield more than expected).

Fields of papers citing papers by Robert E. Banfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert E. Banfield. 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 Robert E. Banfield. The network helps show where Robert E. Banfield may publish in the future.

Co-authors

The 6 scholars most cited alongside Robert E. Banfield, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert E. Banfield Line = papers co-authored together Robert E. Banfield links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 2006297
2 2004151
3 200411
4 20089
5 20048
6 20048
7 20078
8 20037
9 20037
10 20064
11 20083
12 20101

About Robert E. Banfield

Robert E. Banfield is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Molecular Biology and Computer Vision and Pattern Recognition, having authored 12 papers that have together received 514 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (6 papers), Data Mining Algorithms and Applications (5 papers), Machine Learning and Data Classification (5 papers), Anomaly Detection Techniques and Applications (4 papers), Machine Learning in Bioinformatics (1 paper), Data Management and Algorithms (1 paper), Time Series Analysis and Forecasting (1 paper) and Computational Drug Discovery Methods (1 paper). The work is most often cited by research in Artificial Intelligence (323 citations), Computer Vision and Pattern Recognition (104 citations), Signal Processing (49 citations), Health Information Management (19 citations) and Information Systems (88 citations). Robert E. Banfield has collaborated with scholars based in United States. Frequent co-authors include Kevin W. Bowyer, Lawrence Hall, W. Philip Kegelmeyer, Steven A. Eschrich, Richard Collins and Xiao Liu. Their work appears in journals such as Information Fusion, Data Mining and Knowledge Discovery, IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Artificial Intelligence Tools and Proceedings - International Conference on Pattern Recognition.

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.

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