Richard A. Bauder

2.0k total citations · 1 hit paper
31 papers, 1.4k citations indexed

About

Richard A. Bauder is a scholar working on Artificial Intelligence, Health Information Management and Electrical and Electronic Engineering. According to data from OpenAlex, Richard A. Bauder has authored 31 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 12 papers in Health Information Management and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Richard A. Bauder's work include Imbalanced Data Classification Techniques (28 papers), Artificial Intelligence in Healthcare (9 papers) and Anomaly Detection Techniques and Applications (8 papers). Richard A. Bauder is often cited by papers focused on Imbalanced Data Classification Techniques (28 papers), Artificial Intelligence in Healthcare (9 papers) and Anomaly Detection Techniques and Applications (8 papers). Richard A. Bauder collaborates with scholars based in United States. Richard A. Bauder's co-authors include Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya, Matthew Herland, Tawfiq Hasanin, Aaron N. Richter, John Hancock, Huanjing Wang and Qianxin Liang and has published in prestigious journals such as Journal Of Big Data, Health Care Management Science and Health Information Science and Systems.

In The Last Decade

Richard A. Bauder

29 papers receiving 1.3k 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 survey on addressing high-class imbalance in big data

2018 509 citations Joffrey L. Leevy, Taghi M. Khoshgoftaar et al. Journal Of Big Data profile →

Peers

Richard A. Bauder

AI

HIM

EEE

IS

ACCOU

Joffrey L. Leevy United States

Yin Lou United States

Yann‐Aël Le Borgne Belgium

Azuraliza Abu Bakar Malaysia

Alejandro Carrasco Spain

Miriam Seoane Santos Portugal

Haibo He United States

Dennis L. Wilson United States

Suhel Hammoud United Kingdom

Sabah Mohammed Canada

Joffrey L. Leevy United States

Richard A. Bauder

859 ×0.8

AI

99 ×0.3

HIM

156 ×0.7

EEE

117 ×0.9

IS

105 ×0.8

ACCOU

Citations per year, relative to Richard A. Bauder Richard A. Bauder (= 1×) peers Joffrey L. Leevy

Countries citing papers authored by Richard A. Bauder

Since Specialization

Citations

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

Fields of papers citing papers by Richard A. Bauder

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard A. Bauder. 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 Richard A. Bauder. The network helps show where Richard A. Bauder may publish in the future.

Co-authorship network of co-authors of Richard A. Bauder

This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Bauder. A scholar is included among the top collaborators of Richard A. Bauder 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 Richard A. Bauder. Richard A. Bauder 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.

Liang, Qianxin, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2024). Enhancing Medicare Fraud Detection: Random Undersampling Followed by SHAP-Driven Feature Selection with Big Data. 256–263. 1 indexed citations

2.

Hancock, John, Richard A. Bauder, Huanjing Wang, & Taghi M. Khoshgoftaar. (2023). Explainable machine learning models for Medicare fraud detection. Journal Of Big Data. 10(1). 15 indexed citations

3.

Hasanin, Tawfiq, Taghi M. Khoshgoftaar, Joffrey L. Leevy, & Richard A. Bauder. (2020). Investigating class rarity in big data. Journal Of Big Data. 7(1). 15 indexed citations

4.

Leevy, Joffrey L., Taghi M. Khoshgoftaar, Richard A. Bauder, & Naeem Seliya. (2020). Investigating the relationship between time and predictive model maintenance. Journal Of Big Data. 7(1). 15 indexed citations

5.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2020). A study on rare fraud predictions with big Medicare claims fraud data. Intelligent Data Analysis. 24(1). 141–161. 10 indexed citations

6.

Bauder, Richard A., Matthew Herland, & Taghi M. Khoshgoftaar. (2019). Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. 9–14. 5 indexed citations

7.

Leevy, Joffrey L., Taghi M. Khoshgoftaar, Richard A. Bauder, & Naeem Seliya. (2019). The Effect of Time on the Maintenance of a Predictive Model. 1. 1891–1896. 4 indexed citations

8.

Hasanin, Tawfiq, Taghi M. Khoshgoftaar, Joffrey L. Leevy, & Richard A. Bauder. (2019). Severely imbalanced Big Data challenges: investigating data sampling approaches. Journal Of Big Data. 6(1). 84 indexed citations

9.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2018). The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels.. The Florida AI Research Society. 404–409. 22 indexed citations

10.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2018). The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Information Science and Systems. 6(1). 9–9. 63 indexed citations

11.

Herland, Matthew, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2018). Approaches for identifying U.S. medicare fraud in provider claims data. Health Care Management Science. 23(1). 2–19. 12 indexed citations

12.

Herland, Matthew, Taghi M. Khoshgoftaar, & Richard A. Bauder. (2018). Big Data fraud detection using multiple medicare data sources. Journal Of Big Data. 5(1). 115 indexed citations

13.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2018). Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data. 80–87. 50 indexed citations

14.

Leevy, Joffrey L., Taghi M. Khoshgoftaar, Richard A. Bauder, & Naeem Seliya. (2018). A survey on addressing high-class imbalance in big data. Journal Of Big Data. 5(1). 509 indexed citations breakdown →

15.

Bauder, Richard A., Taghi M. Khoshgoftaar, & Tawfiq Hasanin. (2018). Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection. 137–142. 35 indexed citations

16.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2017). Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming.. The Florida AI Research Society. 417–422. 5 indexed citations

17.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2017). Medicare Fraud Detection Using Machine Learning Methods. 858–865. 64 indexed citations

18.

Herland, Matthew, Richard A. Bauder, & Taghi M. Khoshgoftaar. (2017). Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. 579–588. 24 indexed citations

19.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2017). Multivariate outlier detection in medicare claims payments applying probabilistic programming methods. Health Services and Outcomes Research Methodology. 17(3-4). 256–289. 29 indexed citations

20.

Bauder, Richard A. & Taghi M. Khoshgoftaar. (2016). A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims. 347–354. 28 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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