João Gama

22.0k total citations · 8 hit papers
277 papers, 10.8k citations indexed

About

João Gama is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, João Gama has authored 277 papers receiving a total of 10.8k indexed citations (citations by other indexed papers that have themselves been cited), including 177 papers in Artificial Intelligence, 63 papers in Signal Processing and 46 papers in Information Systems. Recurrent topics in João Gama's work include Data Stream Mining Techniques (118 papers), Machine Learning and Data Classification (52 papers) and Anomaly Detection Techniques and Applications (51 papers). João Gama is often cited by papers focused on Data Stream Mining Techniques (118 papers), Machine Learning and Data Classification (52 papers) and Anomaly Detection Techniques and Applications (51 papers). João Gama collaborates with scholars based in Portugal, Brazil and United States. João Gama's co-authors include Albert Bifet, Indrė Žliobaitė, Mykola Pechenizkiy, Abdelhamid Bouchachia, Pedro Pereira Rodrigues, André C. P. L. F. de Carvalho, Hadi Fanaee‐T, Raquel Sebastião, Luís Moreira-Matias and João Mendes‐Moreira and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Power Systems and Expert Systems with Applications.

In The Last Decade

João Gama

266 papers receiving 10.3k citations

Hit Papers

5 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.

A survey on concept drift adaptation

2014 1.9k citations João Gama, Albert Bifet et al. profile →
Ensemble learning for data stream analysis: A survey

2017 694 citations João Gama et al. profile →
Predicting Taxi–Passenger Demand Using Streaming Data

2013 524 citations João Gama et al. profile →
Knowledge Discovery from Data Streams

2010 337 citations João Gama profile →
Data stream clustering

2013 331 citations Jonathan de Andrade Silva, Elaine R. Faria et al. profile →
On evaluating stream learning algorithms

2012 317 citations João Gama, Raquel Sebastião et al. Machine Learning profile →
Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities

2021 200 citations João Gama et al. profile →
Methods and tools for causal discovery and causal inference

2022 105 citations João Gama et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →

Peers

Countries citing papers authored by João Gama

Since Specialization

Citations

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

Fields of papers citing papers by João Gama

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of João Gama

This figure shows the co-authorship network connecting the top 25 collaborators of João Gama. A scholar is included among the top collaborators of João Gama 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 João Gama. João Gama 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	A Systematic Review on Long-Tailed Learning 2025 ·IEEE Transactions on Neural Networks and Learning Systems ·Chongsheng Zhang, George Almpanidis, Gaojuan Fan, (unknown), (unknown), Ji Liu, Aouaidjia Kamel, Paolo Soda, João Gama	6
2	Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning 2025 ·IEEE Transactions on Neural Networks and Learning Systems ·Teresa Salazar, João Gama, Hélder Araújo, Pedro Henriques Abreu	2
3	Novelty detection for multi-label stream classification under extreme verification latency 2023 ·Applied Soft Computing ·(unknown), Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri	1
4	Data Stream Analytics 2023 ·SHILAP Revista de lepidopterología ·Jesús S. Aguilar–Ruiz, Albert Bifet, João Gama	1
5	DSAA 2021 2021 ·IEEE Computational Intelligence Magazine ·João Gama, Concha Bielza, (unknown), Salvador García, (unknown), (unknown), Jie Lü, (unknown), Ana Paula Appel, Luís Matias, Jaime S. Cardoso, Myra Spiliopoulou, (unknown), Rita P. Ribeiro, Mykola Pechenizkiy, (unknown), (unknown), (unknown), (unknown)	1
6	Data stream analysis: Foundations, major tasks and tools 2021 ·Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery ·(unknown), Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu	74
7	Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods 2020 ·Mathematics ·Saeed Nosratabadi, Amirhosein Mosavi, Puhong Duan, Pedram Ghamisi, Ferdinánd Filip, Shahab S. Band, Uwe Reuter, João Gama, Amir H. Gandomi	98
8	Multi-aspect renewable energy forecasting 2020 ·Information Sciences ·Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee‐T, João Gama	60
9	Detecting Bursts of Activity in Telecommunications. 2019 ·Knowledge Discovery and Data Mining ·Bruno Veloso, Carlos Augusto Paiva da Silva Martins, (unknown), (unknown), João Gama	1
10	Weightless neural networks for open set recognition 2017 ·Machine Learning ·(unknown), João Gama, Felipe M. G. França	27
11	Tensor-based anomaly detection: An interdisciplinary survey 2016 ·Knowledge-Based Systems ·Hadi Fanaee‐T, João Gama	99
12	Detecting Events in Evolving Social Networks through Node Centrality Analysis. 2016 ·Fabíola S. F. Pereira, Sandra de Amo, João Gama	4
13	Event detection from traffic tensors: A hybrid model 2016 ·Neurocomputing ·Hadi Fanaee‐T, João Gama	29
14	Ensembles of Adaptive model rules from high-speed data streams 2014 ·Open Repository of the University of Porto (University of Porto) ·João Duarte, João Gama	11
15	Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, Paris, France, June 28, 2009 2009 ·Knowledge Discovery and Data Mining ·Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla, Mohamed Medhat Gaber	1
16	Change detection in learning histograms from data streams 2007 ·Raquel Sebastião, João Gama	23
17	Pursuing the Best ECOC Dimension for Multiclass Problems. 2007 ·The Florida AI Research Society ·(unknown), João Gama, André C. P. L. F. de Carvalho	1
18	Machine Learning: ECML 2005: 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings (Lecture Notes in Computer Science ... / Lecture Notes in Artificial Intelligence) 2005 ·Springer eBooks ·João Gama, Rui Camacho, Pavel Brazdil, Alípio Jorge, Luı́s Torgo	1
19	Combining classification algorithms 2000 ·AI Communications ·João Gama	22
20	Probabilistic Linear Tree 1997 ·International Conference on Machine Learning ·João Gama	7

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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