Chris Seiffert

2.8k total citations · 1 hit paper
18 papers, 2.1k citations indexed

About

Chris Seiffert is a scholar working on Artificial Intelligence, Information Systems and Software. According to data from OpenAlex, Chris Seiffert has authored 18 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 11 papers in Information Systems and 3 papers in Software. Recurrent topics in Chris Seiffert's work include Machine Learning and Data Classification (14 papers), Imbalanced Data Classification Techniques (13 papers) and Software Engineering Research (6 papers). Chris Seiffert is often cited by papers focused on Machine Learning and Data Classification (14 papers), Imbalanced Data Classification Techniques (13 papers) and Software Engineering Research (6 papers). Chris Seiffert collaborates with scholars based in United States. Chris Seiffert's co-authors include Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano, Andres Folleco, Lili Zhao and Naeem Seliya and has published in prestigious journals such as Information Sciences, IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans and Integrated Computer-Aided Engineering.

In The Last Decade

Chris Seiffert

18 papers receiving 2.0k 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.

RUSBoost: A Hybrid Approach to Alleviating Class Imbalance

2009 1.3k citations Chris Seiffert, Taghi M. Khoshgoftaar et al. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chris Seiffert United States	11	1.3k	468	355	207	200	18	2.1k
Yanmin Sun Canada	7	1.7k 1.3×	473 1.0×	300 0.8×	299 1.4×	46 0.2×	10	2.5k
Jason Van Hulse United States	26	2.8k 2.1×	728 1.6×	861 2.4×	468 2.3×	414 2.1×	55	4.2k
Amri Napolitano United States	26	2.6k 1.9×	658 1.4×	894 2.5×	484 2.3×	431 2.2×	98	4.0k
Zhihua Zhou China	8	1.5k 1.1×	413 0.9×	200 0.6×	584 2.8×	41 0.2×	18	2.3k
José A. Sáez Spain	17	1.5k 1.1×	301 0.6×	241 0.7×	287 1.4×	29 0.1×	43	2.0k
Satchidananda Dehuri India	23	912 0.7×	199 0.4×	309 0.9×	274 1.3×	35 0.2×	159	1.9k
Said Jadid Abdulkadir Malaysia	25	1.1k 0.9×	250 0.5×	310 0.9×	353 1.7×	113 0.6×	110	2.6k

Countries citing papers authored by Chris Seiffert

Since Specialization

Citations

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

Fields of papers citing papers by Chris Seiffert

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris Seiffert. 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 Chris Seiffert. The network helps show where Chris Seiffert may publish in the future.

Co-authorship network of co-authors of Chris Seiffert

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

All Works

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18 of 18 papers shown

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Andres Folleco. (2011). An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Information Sciences. 259. 571–595. 106 indexed citations

Seiffert, Chris, et al.. (2009). Improving Software-Quality Predictions With Data Sampling and Boosting. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans. 39(6). 1283–1294. 90 indexed citations

Seiffert, Chris, Taghi M. Khoshgoftaar, & Jason Van Hulse. (2009). Hybrid sampling for imbalanced data. Integrated Computer-Aided Engineering. 16(3). 193–210. 49 indexed citations

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2009). RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans. 40(1). 185–197. 1317 indexed citations breakdown →

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

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2008). Improving Learner Performance with Data Sampling and Boosting. 8. 452–459. 5 indexed citations

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2008). A Comparative Study of Data Sampling and Cost Sensitive Learning. 46–52. 43 indexed citations

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2008). Resampling or Reweighting: A Comparison of Boosting Implementations. 445–451. 47 indexed citations

Seiffert, Chris, Taghi M. Khoshgoftaar, & Jason Van Hulse. (2008). Hybrid sampling for imbalanced data. 202–207. 39 indexed citations

10.

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano. (2008). RUSBoost: Improving classification performance when training data is skewed. Proceedings - International Conference on Pattern Recognition. 1–4. 223 indexed citations

11.

Folleco, Andres, Taghi M. Khoshgoftaar, Jason Van Hulse, & Chris Seiffert. (2007). Learning from Software Quality Data with Class Imbalance and Noise.. Software Engineering and Knowledge Engineering. 82(2). 487–94. 2 indexed citations

12.

Khoshgoftaar, Taghi M., Chris Seiffert, Jason Van Hulse, Amri Napolitano, & Andres Folleco. (2007). Learning with limited minority class data. 348–353. 75 indexed citations

13.

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Andres Folleco. (2007). An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. 651–658. 21 indexed citations

14.

Khoshgoftaar, Taghi M., Jason Van Hulse, Chris Seiffert, & Lili Zhao. (2007). The multiple imputation quantitative noise corrector. Intelligent Data Analysis. 11(3). 245–263. 5 indexed citations

15.

Khoshgoftaar, Taghi M., Chris Seiffert, & Naeem Seliya. (2006). Labeling Network Event Records for Intrusion Detection in aWireless LAN. 9. 200–206. 3 indexed citations

16.

Khoshgoftaar, Taghi M., Jason Van Hulse, & Chris Seiffert. (2006). A Hybrid Approach to Cleansing Software Measurement Data. 10. 713–722. 6 indexed citations

17.

Hulse, Jason Van, Taghi M. Khoshgoftaar, Chris Seiffert, & Lili Zhao. (2006). Noise Correction using Bayesian Multiple Imputation. 478–483. 2 indexed citations

18.

Hulse, Jason Van, Taghi M. Khoshgoftaar, & Chris Seiffert. (2006). A Comparison of Software Fault Imputation Procedures. 135–142. 7 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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