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.
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
20091.3k citationsChris Seiffert, Taghi M. Khoshgoftaar et al.profile →
Experimental perspectives on learning from imbalanced data
2007571 citationsJason Van Hulse, Taghi M. Khoshgoftaar et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Amri Napolitano
Since
Specialization
Citations
This map shows the geographic impact of Amri Napolitano'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 Amri Napolitano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amri Napolitano more than expected).
This network shows the impact of papers produced by Amri Napolitano. 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 Amri Napolitano. The network helps show where Amri Napolitano may publish in the future.
Co-authorship network of co-authors of Amri Napolitano
This figure shows the co-authorship network connecting the top 25 collaborators of Amri Napolitano.
A scholar is included among the top collaborators of Amri Napolitano 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 Amri Napolitano. Amri Napolitano 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.
Najafabadi, Maryam M., et al.. (2016). RUDY Attack: Detection at the Network Level and Its Important Features. The Florida AI Research Society. 288–293.15 indexed citations
2.
Prusa, Joseph D., Taghi M. Khoshgoftaar, & Amri Napolitano. (2016). Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. The Florida AI Research Society. 614–620.1 indexed citations
3.
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
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
7.
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
8.
Wang, Huanjing, Taghi M. Khoshgoftaar, & Amri Napolitano. (2014). Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction.. Software Engineering and Knowledge Engineering. 540–545.1 indexed citations
9.
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
10.
Gao, Kehan, Taghi M. Khoshgoftaar, & Amri Napolitano. (2013). Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation.. Software Engineering and Knowledge Engineering. 612–617.1 indexed citations
11.
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
Khoshgoftaar, Taghi M., Kehan Gao, & Amri Napolitano. (2011). A Comparative Study of Different Strategies for Predicting Software Quality.. Software Engineering and Knowledge Engineering. 65–70.2 indexed citations
17.
Wang, Huanjing, Taghi M. Khoshgoftaar, & Amri Napolitano. (2011). An Empirical Study of Software Metrics Selection Using Support Vector Machine.. Software Engineering and Knowledge Engineering. 83–88.11 indexed citations
Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2008). Building Useful Models from Imbalanced Data with Sampling and Boosting. The Florida AI Research Society. 306–311.38 indexed citations
20.
Hulse, Jason Van, Taghi M. Khoshgoftaar, & Amri Napolitano. (2007). Experimental perspectives on learning from imbalanced data. 935–942.571 indexed citations breakdown →
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.