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.
Deep learning applications and challenges in big data analytics
20151.8k citationsTaghi M. Khoshgoftaar, Randall Wald et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Randall Wald'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 Randall Wald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Randall Wald more than expected).
This network shows the impact of papers produced by Randall Wald. 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 Randall Wald. The network helps show where Randall Wald may publish in the future.
Co-authorship network of co-authors of Randall Wald
This figure shows the co-authorship network connecting the top 25 collaborators of Randall Wald.
A scholar is included among the top collaborators of Randall Wald 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 Randall Wald. Randall Wald is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Gao, Kehan, Taghi M. Khoshgoftaar, & Randall Wald. (2014). Combining Feature Selection and Ensemble Learning for Software Quality Estimation. The Florida AI Research Society.15 indexed citations
2.
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
3.
Wald, Randall, Taghi M. Khoshgoftaar, & Amri Napolitano. (2014). Optimizing Wrapper-Based Feature Selection for Use on Bioinformatics Data.. The Florida AI Research Society.5 indexed citations
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
6.
Wang, Huanjing, Taghi M. Khoshgoftaar, Randall Wald, & Amri Napolitano. (2013). A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data.. Software Engineering and Knowledge Engineering. 467–472.6 indexed citations
7.
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
Khoshgoftaar, Taghi M., et al.. (2012). Robustness of Threshold-Based Feature Rankers with Data Sampling on Noisy and Imbalanced Data. The Florida AI Research Society.9 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.