W. Nick Street

6.4k total citations · 2 hit papers
93 papers, 4.0k citations indexed

About

W. Nick Street is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, W. Nick Street has authored 93 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Artificial Intelligence, 19 papers in Information Systems and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in W. Nick Street's work include AI in cancer detection (12 papers), Data Mining Algorithms and Applications (11 papers) and Artificial Intelligence in Healthcare (8 papers). W. Nick Street is often cited by papers focused on AI in cancer detection (12 papers), Data Mining Algorithms and Applications (11 papers) and Artificial Intelligence in Healthcare (8 papers). W. Nick Street collaborates with scholars based in United States, South Korea and China. W. Nick Street's co-authors include YongSeog Kim, O. L. Mangasarian, William H. Wolberg, Filippo Menczer, Lian Duan, Eric Xu, Samuel Burer, Yi Zhang, Paul S. Bradley and Patricia Bradley and has published in prestigious journals such as Cancer, Management Science and Expert Systems with Applications.

In The Last Decade

W. Nick Street

90 papers receiving 3.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A streaming ensemble algorithm (SEA) for large-scale classification

2001 778 citations W. Nick Street, YongSeog Kim profile →
Breast Cancer Diagnosis and Prognosis Via Linear Programming

1995 559 citations O. L. Mangasarian, W. Nick Street et al. profile →

Peers

W. Nick Street

AI

CVPR

IS

MB

SP

Charles X. Ling Canada

Maria Carolina Monard Brazil

Rudy Setiono Singapore

Pádraig Cunningham Ireland

Aytuğ Onan Türkiye

Petra Perner Germany

Sholom M. Weiss United States

Theodoros Evgeniou France

Christopher C. Yang United States

Joshua Zhexue Huang China

Charles X. Ling Canada

W. Nick Street

3.3k ×1.3

AI

700 ×0.9

CVPR

1.2k ×2.0

IS

552 ×1.3

MB

297 ×0.7

SP

Citations per year, relative to W. Nick Street W. Nick Street (= 1×) peers Charles X. Ling

Countries citing papers authored by W. Nick Street

Since Specialization

Citations

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

Fields of papers citing papers by W. Nick Street

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by W. Nick Street. 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 W. Nick Street. The network helps show where W. Nick Street may publish in the future.

Co-authorship network of co-authors of W. Nick Street

This figure shows the co-authorship network connecting the top 25 collaborators of W. Nick Street. A scholar is included among the top collaborators of W. Nick Street 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 W. Nick Street. W. Nick Street is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Griffin, Benjamin R., Benjamin D. Horne, Michel Chonchol, et al.. (2024). Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm. Kidney Medicine. 6(12). 100918–100918. 1 indexed citations

2.

Lee, Jisoo, et al.. (2022). Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients. BMC Medical Informatics and Decision Making. 22(1). 115–115. 3 indexed citations

3.

Wang, Wenjun & W. Nick Street. (2018). Modeling and maximizing influence diffusion in social networks for viral marketing. Applied Network Science. 3(1). 6–6. 44 indexed citations

4.

Wang, Wenjun & W. Nick Street. (2014). A novel algorithm for community detection and influence ranking in social networks. 555–560. 12 indexed citations

5.

Street, W. Nick, et al.. (2014). Dynamic Class Prediction with Classifier Based Distance Measure.. 79–90. 1 indexed citations

6.

Street, W. Nick, et al.. (2012). Individualized patient-centered lifestyle recommendations: An expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options. Journal of Biomedical Informatics. 45(6). 1164–1174. 36 indexed citations

7.

Street, W. Nick, et al.. (2011). Divide and Transfer: an Exploration of Segmented Transfer to Detect Wikipedia Vandalism.. International Conference on Machine Learning. 133–144. 1 indexed citations

8.

DeCook, Rhonda, et al.. (2010). Query-based Text Normalization Selection Models for Enhanced Retrieval Accuracy. North American Chapter of the Association for Computational Linguistics. 19–26. 1 indexed citations

9.

Street, W. Nick, et al.. (2007). Building a hospital referral expert system with a Prediction and Optimization-Based Decision Support System algorithm. Journal of Biomedical Informatics. 41(2). 371–386. 32 indexed citations

10.

Zhang, Yi, Samuel Burer, & W. Nick Street. (2006). Ensemble Pruning Via Semi-definite Programming. Journal of Machine Learning Research. 7(48). 1315–1338. 188 indexed citations

11.

Street, W. Nick, et al.. (2005). Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods.. PubMed. 1080–1080. 6 indexed citations

12.

Lee, Kyoung-Mi & W. Nick Street. (2003). An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition. IEEE Transactions on Neural Networks. 14(3). 680–687. 23 indexed citations

13.

Wolberg, William H. & W. Nick Street. (2002). Computer-generated nuclear features compared with axillary lymph node status and tumor size as indicators of breast cancer survival. Human Pathology. 33(11). 1086–1091. 15 indexed citations

14.

Kim, YongSeog & W. Nick Street. (2001). Feature selection in supervised and unsupervised learning via evolutionary search. UMI Dissertation Services eBooks. 3 indexed citations

15.

Lee, Kyoung-Mi & W. Nick Street. (2001). Automatic feature mining for personalized digital image retrieval. 38–43. 5 indexed citations

16.

Street, W. Nick. (1998). A Neural Network Model for Prognostic Prediction. International Conference on Machine Learning. 540–546. 44 indexed citations

17.

Wolberg, William H., et al.. (1997). Indeterminate fine-needle aspiration of the breast. Cancer. 81(2). 129–135. 13 indexed citations

18.

Wolberg, William H., et al.. (1997). Indeterminate fine‐needle aspiration of the breast. Cancer. 81(2). 129–135. 1 indexed citations

19.

Wolberg, William H., W. Nick Street, & O. L. Mangasarian. (1997). Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosis. Cancer. 81(3). 172–179. 12 indexed citations

20.

Bradley, Paul S., O. L. Mangasarian, & W. Nick Street. (1996). Clustering via Concave Minimization. Neural Information Processing Systems. 9. 368–374. 156 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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