Jesse Read

7.7k total citations · 2 hit papers
63 papers, 3.8k citations indexed

About

Jesse Read is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Jesse Read has authored 63 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 12 papers in Signal Processing and 11 papers in Information Systems. Recurrent topics in Jesse Read's work include Data Stream Mining Techniques (22 papers), Text and Document Classification Technologies (18 papers) and Machine Learning and Data Classification (15 papers). Jesse Read is often cited by papers focused on Data Stream Mining Techniques (22 papers), Text and Document Classification Technologies (18 papers) and Machine Learning and Data Classification (15 papers). Jesse Read collaborates with scholars based in France, Spain and Finland. Jesse Read's co-authors include Geoffrey Holmes, Bernhard Pfahringer, Eibe Frank, Albert Bifet, Luca Martino, Heitor Murilo Gomes, Jean Paul Barddal, David Luengo, Talel Abdessalem and Fabrício Enembreck and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Biomedical Engineering and ACM Computing Surveys.

In The Last Decade

Jesse Read

61 papers receiving 3.7k citations

Hit Papers

Classifier chains for multi-label classification 2011 2026 2016 2021 2011 2017 400 800 1.2k
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.

  1. Classifier chains for multi-label classification
    2011 1.4k citations Jesse Read, Bernhard Pfahringer et al. Machine Learning profile →
  2. Adaptive random forests for evolving data stream classification
    2017 473 citations Heitor Murilo Gomes, Albert Bifet et al. Machine Learning profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jesse Read France 24 3.0k 801 742 447 425 63 3.8k
Quanquan Gu United States 32 2.3k 0.8× 900 1.1× 894 1.2× 352 0.8× 214 0.5× 142 3.8k
Bianca Zadrozny United States 22 2.9k 0.9× 643 0.8× 561 0.8× 220 0.5× 220 0.5× 56 3.9k
Lifang He China 33 2.2k 0.7× 1.0k 1.3× 431 0.6× 319 0.7× 211 0.5× 175 4.2k
Nesreen K. Ahmed United States 25 2.1k 0.7× 754 0.9× 467 0.6× 398 0.9× 306 0.7× 77 3.9k
Quanming Yao China 26 2.5k 0.8× 1.5k 1.9× 624 0.8× 163 0.4× 266 0.6× 88 4.3k
Ricardo J. G. B. Campello Brazil 24 2.4k 0.8× 591 0.7× 373 0.5× 209 0.5× 639 1.5× 67 3.4k
Sugato Basu United States 21 2.6k 0.8× 1.4k 1.7× 665 0.9× 246 0.6× 656 1.5× 37 3.6k
Grigorios Tsoumakas Greece 29 4.1k 1.3× 1.6k 1.9× 1.4k 1.9× 872 2.0× 483 1.1× 121 5.4k
Shirish Shevade India 16 1.5k 0.5× 902 1.1× 529 0.7× 417 0.9× 318 0.7× 47 3.4k
Juan J. Rodríguez Spain 25 1.8k 0.6× 699 0.9× 330 0.4× 282 0.6× 393 0.9× 76 3.3k

Countries citing papers authored by Jesse Read

Since Specialization
Citations

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

Fields of papers citing papers by Jesse Read

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jesse Read

This figure shows the co-authorship network connecting the top 25 collaborators of Jesse Read. A scholar is included among the top collaborators of Jesse Read 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 Jesse Read. Jesse Read 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.
Read, Jesse, et al.. (2025). Uncertainty Quantification as a Complementary Latent Health Indicator for Remaining Useful Life Prediction on Turbofan Engines. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
2.
Read, Jesse, et al.. (2024). Autoreplicative random forests with applications to missing value imputation. Machine Learning. 113(10). 7617–7643.
3.
Read, Jesse. (2023). From multi-label learning to cross-domain transfer: a model-agnostic approach. Applied Intelligence. 53(21). 25135–25153. 2 indexed citations
4.
Read, Jesse & Indrė Žliobaitė. (2023). Learning from Data Streams: An Overview and Update. SSRN Electronic Journal. 3 indexed citations
5.
Gomes, Heitor Murilo, et al.. (2022). A Survey on Semi-supervised Learning for Delayed Partially Labelled Data Streams. ACM Computing Surveys. 55(4). 1–42. 38 indexed citations
6.
Montiel, Jacob, Saulo Martiello Mastelini, Heitor Murilo Gomes, et al.. (2022). River: Machine learning for streaming data in python. Journal of Machine Learning Research. 22(110). 1–8. 2 indexed citations
7.
Read, Jesse, et al.. (2022). Multi-output regression with structurally incomplete target labels: A case study of modelling global vegetation cover. Ecological Informatics. 72. 101849–101849. 2 indexed citations
8.
Gomes, Heitor Murilo, Jesse Read, Albert Bifet, & Robert J. Durrant. (2021). Learning from evolving data streams through ensembles of random patches. Knowledge and Information Systems. 63(7). 1597–1625. 17 indexed citations
9.
10.
Read, Jesse, et al.. (2020). Better Sign Language Translation with STMC-Transformer. SPIRE - Sciences Po Institutional REpository. 5975–5989. 91 indexed citations
11.
Gomes, Heitor Murilo, Albert Bifet, Jesse Read, et al.. (2019). Correction to: Adaptive random forests for evolving data stream classification. Machine Learning. 108(10). 1877–1878. 9 indexed citations
12.
Gomes, Heitor Murilo, Jesse Read, Albert Bifet, Jean Paul Barddal, & João Gama. (2019). Machine learning for streaming data. ACM SIGKDD Explorations Newsletter. 21(2). 6–22. 154 indexed citations
13.
Montiel, Jacob, Jesse Read, Albert Bifet, & Talel Abdessalem. (2018). Scikit-Multiflow: A Multi-output Streaming Framework. SPIRE - Sciences Po Institutional REpository. 2 indexed citations
14.
Gomes, Heitor Murilo, Albert Bifet, Jesse Read, et al.. (2017). Adaptive random forests for evolving data stream classification. Machine Learning. 106(9-10). 1469–1495. 473 indexed citations breakdown →
15.
Read, Jesse, et al.. (2016). Echo state hoeffding tree learning. Asian Conference on Machine Learning. 382–397. 1 indexed citations
16.
Read, Jesse, et al.. (2016). Data stream classification using random feature functions and novel method combinations. Journal of Systems and Software. 127. 195–204. 12 indexed citations
17.
Read, Jesse, Indrė Žliobaitė, & Jaakko Hollmén. (2015). Labeling sensing data for mobility modeling. Information Systems. 57. 207–222. 4 indexed citations
18.
Read, Jesse, Fernando Pérez‐Cruz, & Albert Bifet. (2015). Deep learning in partially-labeled data streams. 954–959. 23 indexed citations
19.
Read, Jesse, Luca Martino, & David Luengo. (2012). Efficient Monte Carlo Optimization for Multi-dimensional Classifier Chains. arXiv (Cornell University). 2 indexed citations
20.
Read, Jesse. (2008). A pruned problem transformation method for multi-label classification. 61 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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