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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
Topics
Details

Details

  • Name

    João Gama
  • Cluster

    Computer Science
  • Role

    Research Coordinator
  • Since

    01st April 2009
014
Publications

2020

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

Authors
Bifet, A; Berlingerio, M; Gama, J; Read, J; Nogueira, AR;

Publication
BigMine@KDD

Abstract

2020

A scalable saliency-based feature selection method with instance-level information

Authors
Cancela, B; Bolon Canedo, V; Alonso Betanzos, A; Gama, J;

Publication
KNOWLEDGE-BASED SYSTEMS

Abstract
Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, ...), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.

2020

A scalable saliency-based feature selection method with instance-level information

Authors
Cancela, B; Canedo, VB; Betanzos, AA; Gama, J;

Publication
Knowl. Based Syst.

Abstract

2020

BRIGHT - Drift-Aware Demand Predictions for Taxi Networks

Authors
Saadallah, A; Matias, LM; Sousa, R; Khiari, J; Jenelius, E; Gama, J;

Publication
IEEE Trans. Knowl. Data Eng.

Abstract

2020

BRIGHT-Drift-Aware Demand Predictions for Taxi Networks

Authors
Saadallah, A; Moreira Matias, L; Sousa, R; Khiari, J; Jenelius, E; Gama, J;

Publication
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Abstract
Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as finite training sets or stationary distributions. In this paper, we propose BRIGHT: a drift-aware supervised learning framework to predict demand quantities. BRIGHT aims to provide accurate predictions for short-term horizons through a creative ensemble of time series analysis methods that handles distinct types of concept drift. By selecting neighborhoods dynamically, BRIGHT reduces the likelihood of overfitting. By ensuring diversity among the base learners, BRIGHT ensures a high reduction of variance while keeping bias stable. Experiments were conducted using three large-scale heterogeneous real-world transportation networks in Porto (Portugal), Shanghai (China), and Stockholm (Sweden), as well as with controlled experiments using synthetic data where multiple distinct drifts were artificially induced. The obtained results illustrate the advantages of BRIGHT in relation to state-of-the-art methods for this task.

Supervised
thesis

2019

Trustability in data-driven decision models for Public Policy

Author
Sónia Alexandra Carvalho Teixeira

Institution
UP-FEUP

2019

Information Diffusion in Social Networks

Author
Patrícia Coelho de Carvalho

Institution
UP-FEP

2019

Mobility Patterns From Data

Author
Thiago de Andrade Silva

Institution
UP-FEUP

2019

Use of CRM in Sales Management

Author
Luís Manuel Cardoso Morujão

Institution
UP-FEP

2019

The Microblogging effect on stock market: forecasting trading volume and short term abnormal re turns using sentiment analysis

Author
Pedro Tiago Pombinho Alvarilhão

Institution
UP-FEP