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About

João Gama is Associate Professor of the Faculty of Economy, University of Porto. He is a researcher and vice-director of LIAAD, a group belonging to INESC TEC. He got the PhD degree from the University of Porto, in 2000. He is Senior member of IEEE.

He has worked in several National and European projects on Incremental and Adaptive learning systems, Ubiquitous Knowledge Discovery, Learning from Massive, and Structured Data, etc. He served as Co-Program chair of ECML'2005, DS'2009, ADMA'2009, IDA' 2011, and ECML/PKDD'2015. He served as track chair on Data Streams with ACM SAC from 2007 till 2016. He organized a series of Workshops on Knowledge Discovery from Data Streams with ECML/PKDD, and Knowledge Discovery from Sensor Data with ACM SIGKDD. He is author of several books in Data Mining (in Portuguese) and authored a monograph on Knowledge Discovery from Data Streams. He authored more than 250 peer-reviewed papers in areas related to machine learning, data mining, and data streams. He is a member of the editorial board of international journals ML, DMKD, TKDE, IDA, NGC, and KAIS. He (co-)supervised more than 12 PhD students and 50 Msc students.

Interest
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Details

Details

  • Name

    João Gama
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st April 2009
018
Publications

2021

Statistically robust evaluation of stream-based recommender systems

Authors
Vinagre, J; Jorge, AM; Rocha, C; Gama, J;

Publication
IEEE Transactions on Knowledge and Data Engineering

Abstract

2021

Multi-aspect renewable energy forecasting

Authors
Corizzo, R; Ceci, M; Fanaee T, H; Gama, J;

Publication
Information Sciences

Abstract
The increasing presence of renewable energy plants has created new challenges such as grid integration, load balancing and energy trading, making it fundamental to provide effective prediction models. Recent approaches in the literature have shown that exploiting spatio-temporal autocorrelation in data coming from multiple plants can lead to better predictions. Although tensor models and techniques are suitable to deal with spatio-temporal data, they have received little attention in the energy domain. In this paper, we propose a new method based on the Tucker tensor decomposition, capable of extracting a new feature space for the learning task. For evaluation purposes, we have investigated the performance of predictive clustering trees with the new feature space, compared to the original feature space, in three renewable energy datasets. The results are favorable for the proposed method, also when compared with state-of-the-art algorithms. © 2020 Elsevier Inc.

2021

Classification and Recommendation With Data Streams

Authors
Veloso, B; Gama, J; Malheiro, B;

Publication
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.

2021

Forecasting conditional extreme quantiles for wind energy

Authors
Goncalves, C; Cavalcante, L; Brito, M; Bessa, RJ; Gama, J;

Publication
Electric Power Systems Research

Abstract
Probabilistic forecasting of distribution tails (i.e., quantiles below 0.05 and above 0.95) is challenging for non-parametric approaches since data for extreme events are scarce. A poor forecast of extreme quantiles can have a high impact in various power system decision-aid problems. An alternative approach more robust to data sparsity is extreme value theory (EVT), which uses parametric functions for modelling distribution's tails. In this work, we apply conditional EVT estimators to historical data by directly combining gradient boosting trees with a truncated generalized Pareto distribution. The parametric function parameters are conditioned by covariates such as wind speed or direction from a numerical weather predictions grid. The results for a wind power plant located in Galicia, Spain, show that the proposed method outperforms state-of-the-art methods in terms of quantile score. © 2020 Elsevier B.V.

2021

Hyperparameter self-tuning for data streams

Authors
Veloso, B; Gama, J; Malheiro, B; Vinagre, J;

Publication
Information Fusion

Abstract

Supervised
thesis

2020

Renewable Energy Forecasting – Extreme Quantiles, Data Privacy and Monetization

Author
Carla Sofia da Silva Gonçalves

Institution
UP-FCUP

2020

Learning the dynamics of voting behaviour during a general election campaign using a dynamic Bayesian network

Author
Patrício Ricardo Soares Costa

Institution
UP-FEP

2020

Incremental Temporal Interval Mining Methodologies

Author
Ana Micaela Gomes Batista

Institution
UP-FCUP

2020

Mobility Patterns from Data

Author
Thiago de Andrade Silva

Institution
UP-FEUP

2020

Predictive Maintenance for Air Production Unit in EuroTram Vehicles

Author
Mariana Chaves de Barros

Institution
UP-FEP