David J. Dittman

833 total citations
38 papers, 612 citations indexed

About

David J. Dittman is a scholar working on Molecular Biology, Artificial Intelligence and Information Systems. According to data from OpenAlex, David J. Dittman has authored 38 papers receiving a total of 612 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 26 papers in Artificial Intelligence and 13 papers in Information Systems. Recurrent topics in David J. Dittman's work include Gene expression and cancer classification (25 papers), Machine Learning and Data Classification (16 papers) and Imbalanced Data Classification Techniques (15 papers). David J. Dittman is often cited by papers focused on Gene expression and cancer classification (25 papers), Machine Learning and Data Classification (16 papers) and Imbalanced Data Classification Techniques (15 papers). David J. Dittman collaborates with scholars based in United States. David J. Dittman's co-authors include Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald, Joseph D. Prusa, Jason Van Hulse, Huanjing Wang, Andrew G. Roth, Shantel Olivares, Lawrence J. Jennings and Klaus J. Busam and has published in prestigious journals such as The American Journal of Surgical Pathology, Acta Neuropathologica Communications and Journal of Cutaneous Pathology.

In The Last Decade

David J. Dittman

37 papers receiving 600 citations

Peers

David J. Dittman

CVPR

HIM

Barbara Pes Italy

Fazel Famili Canada

Márcio P. Basgalupp Brazil

Pablo Bermejo Spain

Quinlan United States

Bhavani Raskutti Australia

Lawrence Shih United States

Guangtao Wang China

Gianvito Pio Italy

Abeer ElKorany Egypt

Barbara Pes Italy

David J. Dittman

270 ×0.6

154 ×0.8

77 ×0.6

125 ×1.2

CVPR

30 ×0.8

HIM

Citations per year, relative to David J. Dittman David J. Dittman (= 1×) peers Barbara Pes

Countries citing papers authored by David J. Dittman

Since Specialization

Citations

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

Fields of papers citing papers by David J. Dittman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Dittman. 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 David J. Dittman. The network helps show where David J. Dittman may publish in the future.

Co-authorship network of co-authors of David J. Dittman

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

All Works

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20 of 20 papers shown

Olivares, Shantel, Lawrence J. Jennings, David J. Dittman, et al.. (2023). Agminated presentation of fusion‐driven melanocytic neoplasms. Journal of Cutaneous Pathology. 50(10). 913–921. 2 indexed citations

McCord, Matthew, Pouya Jamshidi, Lucas Santana‐Santos, et al.. (2023). Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results. Acta Neuropathologica Communications. 11(1). 175–175. 1 indexed citations

Dittman, David J., Taghi M. Khoshgoftaar, & Amri Napolitano. (2015). Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data. The Florida AI Research Society. 329–334. 5 indexed citations

Prusa, Joseph D., Taghi M. Khoshgoftaar, & David J. Dittman. (2015). Impact of Feature Selection Techniques for Tweet Sentiment Classification.. The Florida AI Research Society. 299–304. 38 indexed citations

Prusa, Joseph D., Taghi M. Khoshgoftaar, David J. Dittman, & Amri Napolitano. (2015). Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. 197–202. 104 indexed citations

Khoshgoftaar, Taghi M., et al.. (2015). Investigating New Bootstrapping Approaches of Bagging Classifiers to Account for Class Imbalance in Bioinformatics Datasets. 987–994. 1 indexed citations

Dittman, David J., et al.. (2015). Choosing an Appropriate Ensemble Classifier for Balanced Bioinformatics Data. 17–24.

Dittman, David J., Taghi M. Khoshgoftaar, & Amri Napolitano. (2015). The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data. 457–463. 32 indexed citations

Dittman, David J., Taghi M. Khoshgoftaar, Randall Wald, & Amri Napolitano. (2014). Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data. The Florida AI Research Society. 29 indexed citations

10.

Khoshgoftaar, Taghi M., et al.. (2014). Effects of the Use of Boosting on Classification Performance of Imbalanced Bioinformatics Datasets. 420–426. 5 indexed citations

11.

Dittman, David J., et al.. (2014). Select-Bagging: Effectively Combining Gene Selection and Bagging for Balanced Bioinformatics Data. 14. 413–419. 3 indexed citations

12.

Dittman, David J., Taghi M. Khoshgoftaar, & Amri Napolitano. (2014). Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets. 304–310. 11 indexed citations

13.

Wald, Randall, Taghi M. Khoshgoftaar, & David J. Dittman. (2013). Ensemble Gene Selection Versus Single Gene Selection: Which Is Better?. The Florida AI Research Society. 3 indexed citations

14.

Dittman, David J., Taghi M. Khoshgoftaar, Randall Wald, & Amri Napolitano. (2013). Classification Performance of Rank Aggregation Techniques for Ensemble Gene Selection.. The Florida AI Research Society. 12 indexed citations

15.

Khoshgoftaar, Taghi M., et al.. (2012). A review of the stability of feature selection techniques for bioinformatics data. 356–363. 87 indexed citations

16.

Wald, Randall, et al.. (2012). An extensive comparison of feature ranking aggregation techniques in bioinformatics. 377–384. 29 indexed citations

17.

Khoshgoftaar, Taghi M., et al.. (2012). The Effect of Number of Iterations on Ensemble Gene Selection. 198–203. 4 indexed citations

18.

Khoshgoftaar, Taghi M., et al.. (2012). First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques. 151–157. 33 indexed citations

19.

Dittman, David J., Taghi M. Khoshgoftaar, Randall Wald, & Huanjing Wang. (2011). Stability Analysis of Feature Ranking Techniques on Biological Datasets. 62. 252–256. 19 indexed citations

20.

Dittman, David J., Taghi M. Khoshgoftaar, Randall Wald, & Jason Van Hulse. (2010). Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques. 147–152. 42 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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