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ORCIDJoão Gama 0001
João Manuel Portela da Gama
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- affiliation: University of Porto, Laboratory of Artificial Intelligence and Decision Support (LIAAD), Portugal
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2020 – today
- 2024
[j132]Saulo Martiello Mastelini
, Bruno Veloso, Max Halford, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
SWINN: Efficient nearest neighbor search in sliding windows using graphs. Inf. Fusion 101: 101979 (2024)- 2023
- [j131]Joel D. Costa, Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Novelty detection for multi-label stream classification under extreme verification latency. Appl. Soft Comput. 141: 110265 (2023) - [j130]Shazia Tabassum, João Gama, Paulo J. Azevedo, Mário Cordeiro, Carlos Martins, Andre Martins:
Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs. Expert Syst. J. Knowl. Eng. 40(5) (2023) - [j129]Jorge Meira, Bruno Veloso, Verónica Bolón-Canedo, Goreti Marreiros, Amparo Alonso-Betanzos, João Gama:
Data-driven predictive maintenance framework for railway systems. Intell. Data Anal. 27(4): 1087-1102 (2023) - [j128]Sofia Fernandes, Hadi Fanaee-T, João Gama, Leo Tisljaric, Tomislav Smuc:
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks. Mach. Learn. 112(2): 459-481 (2023) - [j127]Jie Lu, João Gama, Xin Yao, Leandro L. Minku:
Guest Editorial: Special Issue on Stream Learning. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6683-6685 (2023) - [j126]Paula Raissa Silva, João Vinagre, João Gama:
Towards federated learning: An overview of methods and applications. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [c223]Arijit Ukil, João Gama, Antonio J. Jara, Leandro Marín:
Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond. CIKM 2023: 5296-5299 - [c222]Thiago Andrade, João Gama:
Which Way to Go - Finding Frequent Trajectories Through Clustering. DS 2023: 460-473 - [c221]Rafael Mamede, Nuno Paiva, João Gama:
Error Analysis on Industry Data: Using Weak Segment Detection for Local Model Agnostic Prediction Intervals. DS 2023: 661-672 - [c220]Inês Martins, João S. Resende, João Gama:
Online Influence Forest for Streaming Anomaly Detection. IDA 2023: 274-286 - [c219]João Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso:
XAI for Predictive Maintenance. KDD 2023: 5798-5799 - [c218]Miguel E. P. Silva, Bruno Veloso, João Gama:
Predictive Maintenance, Adversarial Autoencoders and Explainability. ECML/PKDD (7) 2023: 260-275 - [c217]Paula Raissa Silva, João Vinagre, João Gama:
A DTW Approach for Complex Data A Case Study with Network Data Streams. SAC 2023: 402-409 - [c216]Thiago Andrade, João Gama:
Estimating Instantaneous Vehicle Emissions. SAC 2023: 422-424 - [c215]Szymon Bobek, Slawomir Nowaczyk, João Gama, Sepideh Pashami, Rita P. Ribeiro, Zahra Taghiyarrenani, Bruno Veloso, Lala H. Rajaoarisoa, Maciej Szelazek, Grzegorz J. Nalepa:
Why Industry 5.0 Needs XAI 2.0? xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 1-6 - [e32]Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu:
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings. Lecture Notes in Computer Science 14276, Springer 2023, ISBN 978-3-031-45274-1 [contents] - [e31]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Communications in Computer and Information Science 1752, Springer 2023, ISBN 978-3-031-23617-4 [contents] - [e30]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Communications in Computer and Information Science 1753, Springer 2023, ISBN 978-3-031-23632-7 [contents] - [d3]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
MetroPT-3 Dataset. UCI Machine Learning Repository, 2023
- [i26]Angelica Liguori, Luciano Caroprese, Marco Minici, Bruno Veloso, Francesco Spinnato, Mirco Nanni, Giuseppe Manco, João Gama:
Modeling Events and Interactions through Temporal Processes - A Survey. CoRR abs/2303.06067 (2023) - [i25]Longbing Cao, Hui Chen, Xuhui Fan, João Gama, Yew-Soon Ong, Vipin Kumar:
Bayesian Federated Learning: A Survey. CoRR abs/2304.13267 (2023) - [i24]Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski, Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi, Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed Mouchaweh, Lala H. Rajaoarisoa, Grzegorz J. Nalepa, João Gama:
Explainable Predictive Maintenance. CoRR abs/2306.05120 (2023) - 2022
- [j125]João Gama, Rita P. Ribeiro, Bruno Veloso:
Data-Driven Predictive Maintenance. IEEE Intell. Syst. 37(4): 27-29 (2022) - [j124]Inês Martins, João S. Resende, Patrícia R. Sousa, Simão Silva, Luis Antunes, João Gama:
Host-based IDS: A review and open issues of an anomaly detection system in IoT. Future Gener. Comput. Syst. 133: 95-113 (2022) - [j123]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Semi-causal decision trees. Prog. Artif. Intell. 11(1): 105-119 (2022) - [j122]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-Francois Marteau:
Open challenges for Machine Learning based Early Decision-Making research. SIGKDD Explor. 24(2): 12-31 (2022) - [j121]Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama:
Methods and tools for causal discovery and causal inference. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [c214]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis. EPIA 2022: 587-598 - [c213]Thiago Andrade, João Gama:
How are you Riding? Transportation Mode Identification from Raw GPS Data. EPIA 2022: 648-659 - [c212]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance. ICEGOV 2022: 25-31 - [c211]Ricardo Cerri, Elaine R. Faria, João Gama:
An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps. ICMLA 2022: 1071-1076 - [c210]Narjes Davari, Sepideh Pashami, Bruno Veloso, Slawomir Nowaczyk, Yuantao Fan, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. IDA 2022: 39-52 - [c209]Nirbhaya Shaji, João Gama, Rita P. Ribeiro, Pedro Gomes:
Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph. IDA 2022: 278-289 - [c208]Sérgio M. Jesus, José Pombal, Duarte Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. NeurIPS 2022 - [c207]Rodrigo Salles, Jérôme Mendes, Rita P. Ribeiro, João Gama:
Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data. PKDD/ECML Workshops (1) 2022: 55-70 - [c206]Sónia Teixeira, Bruno Veloso, José Coelho Rodrigues, João Gama:
Ethical and Technological AI Risks Classification: A Human Vs Machine Approach. PKDD/ECML Workshops (1) 2022: 150-166 - [c205]Nirbhaya Shaji, Thiago Andrade, Rita P. Ribeiro, João Gama:
Study on Correlation Between Vehicle Emissions and Air Quality in Porto. PKDD/ECML Workshops (1) 2022: 181-196 - [c204]Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Ehsan Aminian, Bruno Veloso, João Gama:
Online Anomaly Explanation: A Case Study on Predictive Maintenance. PKDD/ECML Workshops (2) 2022: 383-399 - [c203]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. PKDD/ECML Workshops (2) 2022: 400-409 - [c202]Emanuel Sousa Tomé, Rita P. Ribeiro, Bruno Veloso, João Gama:
An Online Data-Driven Predictive Maintenance Approach for Railway Switches. PKDD/ECML Workshops (2) 2022: 410-422 - [e29]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13280, Springer 2022, ISBN 978-3-031-05932-2 [contents] - [e28]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13281, Springer 2022, ISBN 978-3-031-05935-3 [contents] - [e27]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13282, Springer 2022, ISBN 978-3-031-05980-3 [contents] - [d2]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT: A Benchmark dataset for predictive maintenance. Version 1. Zenodo, 2022 [all versions] - [d1]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT2: A Benchmark dataset for predictive maintenance. Version V2. Zenodo, 2022 [all versions] - [i23]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau:
Open challenges for Machine Learning based Early Decision-Making research. CoRR abs/2204.13111 (2022) - [i22]Paula Raissa Silva, João Vinagre, João Gama:
Federated Anomaly Detection over Distributed Data Streams. CoRR abs/2205.07829 (2022) - [i21]Rui Portocarrero Sarmento, Douglas de O. Cardoso, João Gama, Pavel Brazdil:
Contextualization for the Organization of Text Documents Streams. CoRR abs/2206.02632 (2022) - [i20]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Mota Pereira:
A Benchmark dataset for predictive maintenance. CoRR abs/2207.05466 (2022) - [i19]Sérgio M. Jesus, José Pombal, Duarte Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. CoRR abs/2211.13358 (2022) - [i18]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
Humans Versus Machines: The Perspective of Two Different Approaches in Classification for Ethical Design. ERCIM News 2022(131) (2022) - 2021
- [j120]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Tensor decomposition for analysing time-evolving social networks: an overview. Artif. Intell. Rev. 54(4): 2891-2916 (2021) - [j119]Adrien Bécue, Isabel Praça, João Gama:
Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artif. Intell. Rev. 54(5): 3849-3886 (2021) - [j118]Ehsan Aminian, Rita P. Ribeiro, João Gama:
Chebyshev approaches for imbalanced data streams regression models. Data Min. Knowl. Discov. 35(6): 2389-2466 (2021) - [j117]Paulo Paraíso, Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Using network features for credit scoring in microfinance. Int. J. Data Sci. Anal. 12(2): 121-134 (2021) - [j116]Bruno Veloso, João Gama, Benedita Malheiro, João Vinagre:
Hyperparameter self-tuning for data streams. Inf. Fusion 76: 75-86 (2021) - [j115]Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee-T, João Gama:
Multi-aspect renewable energy forecasting. Inf. Sci. 546: 701-722 (2021) - [j114]Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Sensors 21(17): 5739 (2021) - [j113]Rui Portocarrero Sarmento, Douglas de O. Cardoso, Kemmily Dearo, Pavel Brazdil, João Gama:
Text documents streams with improved incremental similarity. Soc. Netw. Anal. Min. 11(1): 113 (2021) - [j112]João Vinagre, Alípio Mário Jorge, Conceição Rocha, João Gama:
Statistically Robust Evaluation of Stream-Based Recommender Systems. IEEE Trans. Knowl. Data Eng. 33(7): 2971-2982 (2021) - [j111]Maroua Bahri, Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu:
Data stream analysis: Foundations, major tasks and tools. WIREs Data Mining Knowl. Discov. 11(3) (2021) - [c201]João Gama, Bruno Veloso, Ehsan Aminian, Rita P. Ribeiro:
Current Trends in Learning from Data Streams. BDA 2021: 183-193 - [c200]Jean-Gabriel Gaudreault, Paula Branco, João Gama:
An Analysis of Performance Metrics for Imbalanced Classification. DS 2021: 67-77 - [c199]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. DSAA 2021: 1-10 - [c198]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama, Alberto A. Pinto:
Generalised Partial Association in Causal Rules Discovery. EPIA 2021: 485-497 - [c197]Shazia Tabassum, João Gama, Paulo Azevedo, Luis Teixeira, Carlos Martins, Andre Martins:
Dynamic Topic Modeling Using Social Network Analytics. EPIA 2021: 498-509 - [c196]Patrício Costa, Ana Rita Nogueira, João Gama:
Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks. EPIA 2021: 524-536 - [c195]Sérgio M. Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer?: An Application-grounded Evaluation of Post-hoc Explanations. FAccT 2021: 805-815 - [c194]Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama:
Hyper-parameter Optimization for Latent Spaces. ECML/PKDD (3) 2021: 249-264 - [c193]Sónia Teixeira, Guilherme Londres, Bruno Veloso, Rita P. Ribeiro, João Gama:
Improving Smart Waste Collection Using AutoML. PKDD/ECML Workshops (2) 2021: 283-298 - [c192]Ricardo Cerri, Joel David Costa Júnior, Elaine R. Faria, João Gama:
A new self-organizing map based algorithm for multi-label stream classification. SAC 2021: 418-426 - [e26]Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama:
Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings. Lecture Notes in Computer Science 12695, Springer 2021, ISBN 978-3-030-74250-8 [contents] - [e25]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e24]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee A. D. Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i17]Sérgio M. Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations. CoRR abs/2101.08758 (2021) - [i16]Douglas Castilho, Tharsis T. P. Souza, Soong Moon Kang, João Gama, André C. P. L. F. de Carvalho:
Forecasting Financial Market Structure from Network Features using Machine Learning. CoRR abs/2110.11751 (2021) - 2020
- [j110]Albert Bifet, João Gama:
IoT data stream analytics. Ann. des Télécommunications 75(9-10): 491-492 (2020) - [j109]Thiago Andrade, Brais Cancela, João Gama:
Discovering locations and habits from human mobility data. Ann. des Télécommunications 75(9-10): 505-521 (2020) - [j108]Shazia Tabassum, Muhammad Ajmal Azad, João Gama:
Profiling high leverage points for detecting anomalous users in telecom data networks. Ann. des Télécommunications 75(9-10): 573-581 (2020) - [j107]Bruno Veloso, Shazia Tabassum, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Interconnect bypass fraud detection: a case study. Ann. des Télécommunications 75(9-10): 583-596 (2020) - [j106]Felipe Azevedo Pinage, Eulanda Miranda dos Santos, João Gama:
A drift detection method based on dynamic classifier selection. Data Min. Knowl. Discov. 34(1): 50-74 (2020) - [j105]Sofia Fernandes, Hadi Fanaee-T, João Gama:
NORMO: A new method for estimating the number of components in CP tensor decomposition. Eng. Appl. Artif. Intell. 96: 103926 (2020) - [j104]Thiago Andrade, Brais Cancela, João Gama:
From mobility data to habits and common pathways. Expert Syst. J. Knowl. Eng. 37(6) (2020) - [j103]Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama:
A scalable saliency-based feature selection method with instance-level information. Knowl. Based Syst. 192: 105326 (2020) - [j102]Shazia Tabassum, Bruno Veloso, João Gama:
On fast and scalable recurring link's prediction in evolving multi-graph streams. Netw. Sci. 8(S1): S65-S81 (2020) - [j101]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Trans. Knowl. Data Eng. 32(2): 234-245 (2020) - [c191]Maroua Bahri, Bruno Veloso, Albert Bifet, João Gama:
AutoML for Stream k-Nearest Neighbors Classification. IEEE BigData 2020: 597-602 - [c190]Teru Fujii, Masahito Kumano, João Gama, Masahiro Kimura:
Detecting Geographical Competitive Structure for POI Visit Dynamics. COMPLEX NETWORKS (2) 2020: 27-38 - [c189]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features. DS 2020: 207-216 - [c188]Leo Tisljaric, Sofia Fernandes, Tonci Caric, João Gama:
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. DS 2020: 674-688 - [c187]Paulo Paraíso, Pedro Gomes, Saulo Ruiz, Luís Rodrigues, João Gama:
Using Network Features for Credit Scoring in MicroFinance: Extended Abstract. DSAA 2020: 783-784 - [c186]Ana Rita Nogueira, João Gama, Carlos Abreu Ferreira:
Improving Prediction with Causal Probabilistic Variables. IDA 2020: 379-390 - [c185]Bruno Veloso, Benedita Malheiro, Juan-Carlos Burguillo, João Gama:
Impact of Trust and Reputation Based Brokerage on the CloudAnchor Platform. PAAMS 2020: 303-314 - [c184]Bruno Veloso, João Gama:
Self Hyper-parameter Tuning for Stream Classification Algorithms. IoT Streams/ITEM@PKDD/ECML 2020: 3-13 - [c183]Mariana Barros, Bruno Veloso, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Detection of an Air Production Unit in Operational Context. IoT Streams/ITEM@PKDD/ECML 2020: 61-74 - [c182]Bruno Veloso, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Fraud detection using heavy hitters: a case study. SAC 2020: 482-489 - [e23]Albert Bifet, Michele Berlingerio, João Gama, Jesse Read, Ana Rita Nogueira:
Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019. CEUR Workshop Proceedings 2579, CEUR-WS.org 2020 [contents] - [e22]Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla:
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [e21]João Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed Mouchaweh, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott:
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Communications in Computer and Information Science 1325, Springer 2020, ISBN 978-3-030-66769-6 [contents] - [i15]