Jean Paul Barddal

2.5k total citations · 2 hit papers
56 papers, 1.6k citations indexed

About

Jean Paul Barddal is a scholar working on Artificial Intelligence, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Jean Paul Barddal has authored 56 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Artificial Intelligence, 16 papers in Signal Processing and 7 papers in Computer Networks and Communications. Recurrent topics in Jean Paul Barddal's work include Data Stream Mining Techniques (40 papers), Anomaly Detection Techniques and Applications (19 papers) and Machine Learning and Data Classification (18 papers). Jean Paul Barddal is often cited by papers focused on Data Stream Mining Techniques (40 papers), Anomaly Detection Techniques and Applications (19 papers) and Machine Learning and Data Classification (18 papers). Jean Paul Barddal collaborates with scholars based in Brazil, France and New Zealand. Jean Paul Barddal's co-authors include Heitor Murilo Gomes, Fabrício Enembreck, Albert Bifet, Jesse Read, Talel Abdessalem, Geoffrey Holmes, Bernhard Pfahringer, João Gama, Alceu de Souza Britto and Denise Maria Vecino Sato and has published in prestigious journals such as Expert Systems with Applications, ACM Computing Surveys and Neurocomputing.

In The Last Decade

Jean Paul Barddal

51 papers receiving 1.5k 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.

Adaptive random forests for evolving data stream classification

2017 473 citations Heitor Murilo Gomes, Albert Bifet et al. Machine Learning profile →
A Survey on Ensemble Learning for Data Stream Classification

2017 395 citations Heitor Murilo Gomes, Jean Paul Barddal et al. ACM Computing Surveys profile →

Peers

Countries citing papers authored by Jean Paul Barddal

Since Specialization

Citations

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

Fields of papers citing papers by Jean Paul Barddal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean Paul Barddal. 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 Jean Paul Barddal. The network helps show where Jean Paul Barddal may publish in the future.

Co-authorship network of co-authors of Jean Paul Barddal

This figure shows the co-authorship network connecting the top 25 collaborators of Jean Paul Barddal. A scholar is included among the top collaborators of Jean Paul Barddal 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 Jean Paul Barddal. Jean Paul Barddal 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

#	Work	Indexed citations
1	Behavioral insights of adaptive splitting decision trees in evolving data stream classification 2025 ·Knowledge and Information Systems ·(unknown), Jean Paul Barddal, Fabrício Enembreck	0
2	Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review 2024 ·ACM Transactions on Intelligent Systems and Technology ·(unknown), (unknown), Alessandro L. Koerich, Alceu de Souza Britto, Jean Paul Barddal	1
3	Just Change on Change: Adaptive Splitting Time for Decision Trees in Data Stream Classification 2024 ·(unknown), Jean Paul Barddal, Fabrício Enembreck	1
4	Fuels Demand Forecasting: Identifying Leading Feature Sets, Prediction Strategy, and Regressors 2024 ·Jonas Krause, (unknown), Jean Paul Barddal, Alceu S. Britto, Vinícius M. A. Souza	0
5	An explainable machine learning approach for student dropout prediction 2023 ·Expert Systems with Applications ·(unknown), Alceu de Souza Britto, Jean Paul Barddal	27
6	Random forest kernel for high-dimension low sample size classification 2023 ·Statistics and Computing ·(unknown), Simon Bernard, Jean Paul Barddal, Laurent Heutte	6
7	A systematic review on computer vision-based parking lot management applied on public datasets 2022 ·Expert Systems with Applications ·Paulo Ricardo Lisboa de Almeida, (unknown), Rafael Stubs Parpinelli, Jean Paul Barddal	30
8	Exploring diversity in data complexity and classifier decision spaces for pool generation 2022 ·Information Fusion ·(unknown), Alceu S. Britto, Jean Paul Barddal, Luiz S. Oliveira, Robert Sabourin	7
9	Automatic disease vector mosquitoes identification via hierarchical data stream classification 2022 ·Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing ·(unknown), Jean Paul Barddal, Júlio César Nievola	1
10	A Survey on Concept Drift in Process Mining 2021 ·ACM Computing Surveys ·Denise Maria Vecino Sato, (unknown), Jean Paul Barddal, Edson Emílio Scalabrin	59
11	Regularized and incremental decision trees for data streams 2020 ·Annals of Telecommunications ·Jean Paul Barddal, Fabrício Enembreck	3
12	Boosting decision stumps for dynamic feature selection on data streams 2019 ·Information Systems ·Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer	26
13	Correction to: Adaptive random forests for evolving data stream classification 2019 ·Machine Learning ·Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoffrey Holmes, Talel Abdessalem	9
14	Vertical and Horizontal Partitioning in Data Stream Regression Ensembles 2019 ·Jean Paul Barddal	3
15	Machine learning for streaming data 2019 ·ACM SIGKDD Explorations Newsletter ·Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal, João Gama	154
16	Merit-guided dynamic feature selection filter for data streams 2018 ·Expert Systems with Applications ·Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer	34
17	Adaptive random forests for evolving data stream classification breakdown → 2017 ·Machine Learning ·Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, (unknown), Geoffrey Holmes, Talel Abdessalem	473
18	A Survey on Ensemble Learning for Data Stream Classification breakdown → 2017 ·ACM Computing Surveys ·Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, Albert Bifet	395
19	A survey on feature drift adaptation: Definition, benchmark, challenges and future directions 2016 ·Journal of Systems and Software ·Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer	82
20	SNCStream+: Extending a high quality true anytime data stream clustering algorithm 2016 ·Information Systems ·Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Angéla Barthes	14

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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