Robert E. Banfield

846 total citations
12 papers, 514 citations indexed

About

Robert E. Banfield is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Robert E. Banfield has authored 12 papers receiving a total of 514 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 5 papers in Information Systems and 2 papers in Signal Processing. Recurrent topics in Robert E. Banfield's work include Data Stream Mining Techniques (6 papers), Machine Learning and Data Classification (5 papers) and Data Mining Algorithms and Applications (5 papers). Robert E. Banfield is often cited by papers focused on Data Stream Mining Techniques (6 papers), Machine Learning and Data Classification (5 papers) and Data Mining Algorithms and Applications (5 papers). Robert E. Banfield collaborates with scholars based in United States. Robert E. Banfield's co-authors include Lawrence Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Richard Collins, Steven A. Eschrich and Xiao Liu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Fusion and Data Mining and Knowledge Discovery.

In The Last Decade

Robert E. Banfield

12 papers receiving 492 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Robert E. Banfield United States	8	323	104	88	49	34	12	514
Alireza Farhangfar Canada	6	276 0.9×	75 0.7×	139 1.6×	66 1.3×	27 0.8×	8	478
Yangguang Liu China	11	220 0.7×	96 0.9×	52 0.6×	63 1.3×	28 0.8×	52	468
Xuewen Chen United States	8	357 1.1×	99 1.0×	65 0.7×	27 0.6×	71 2.1×	19	507
Tieli Sun China	11	275 0.9×	93 0.9×	82 0.9×	29 0.6×	21 0.6×	30	488
Alex Aussem France	14	299 0.9×	104 1.0×	51 0.6×	56 1.1×	55 1.6×	25	501
I. Cloete South Africa	12	303 0.9×	61 0.6×	64 0.7×	32 0.7×	23 0.7×	37	504
B. Venkatesh India	5	202 0.6×	74 0.7×	59 0.7×	39 0.8×	32 0.9×	20	475
N. Ramaraj India	9	188 0.6×	102 1.0×	79 0.9×	40 0.8×	55 1.6×	28	390
Qinpei Zhao China	10	262 0.8×	96 0.9×	76 0.9×	89 1.8×	23 0.7×	49	510
Younès Bennani France	16	489 1.5×	206 2.0×	66 0.8×	138 2.8×	41 1.2×	94	673

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-authorship network of co-authors of Robert E. Banfield

This figure shows the co-authorship network connecting the top 25 collaborators of Robert E. Banfield. A scholar is included among the top collaborators of Robert E. Banfield based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Robert E. Banfield. Robert E. Banfield is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

Banfield, Robert E., et al.. (2010). Detecting and ordering salient regions. Data Mining and Knowledge Discovery. 22(1-2). 259–290. 1 indexed citations

Banfield, Robert E., et al.. (2008). Semi-supervised learning on large complex simulations. Proceedings - International Conference on Pattern Recognition. 1–4. 9 indexed citations

Banfield, Robert E., et al.. (2008). Detecting and ordering salient regions for efficient browsing. Proceedings - International Conference on Pattern Recognition. 1–4. 3 indexed citations

Banfield, Robert E., et al.. (2007). Using classifier ensembles to label spatially disjoint data. Information Fusion. 9(1). 120–133. 8 indexed citations

Banfield, Robert E., Lawrence Hall, Kevin W. Bowyer, & W. Philip Kegelmeyer. (2006). A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(1). 173–180. 297 indexed citations

Banfield, Robert E., et al.. (2006). Learning to Predict Salient Regions from Disjoint and Skewed Training Sets. 116–126. 4 indexed citations

Hall, Lawrence, et al.. (2004). Comparing pure parallel ensemble creation techniques against bagging. 533–536. 11 indexed citations

Hall, Lawrence, Xiao Liu, Kevin W. Bowyer, & Robert E. Banfield. (2004). Why are neural networks sometimes much more accurate than decision trees: an analysis on a bio-informatics problem. 3. 2851–2856. 8 indexed citations

Banfield, Robert E., Lawrence Hall, Kevin W. Bowyer, & W. Philip Kegelmeyer. (2004). Ensemble diversity measures and their application to thinning. Information Fusion. 6(1). 49–62. 151 indexed citations

10.

Banfield, Robert E.. (2004). Ensemble diversity measures and their application to thinning. Information Fusion. 8 indexed citations

11.

Hall, Lawrence, Richard Collins, Kevin W. Bowyer, & Robert E. Banfield. (2003). Error-based pruning of decision trees grown on very large data sets can work!. 233–238. 7 indexed citations

12.

Hall, Lawrence, Kevin W. Bowyer, Robert E. Banfield, Steven A. Eschrich, & Richard Collins. (2003). Is Error-Based Pruning Redeemable?. International Journal of Artificial Intelligence Tools. 12(3). 249–264. 7 indexed citations

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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