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About

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
010
Publications

2019

Self Hyper-parameter Tuning for Stream Recommendation Algorithms

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

Publication
Metasomatic Textures in Granites - Springer Mineralogy

Abstract

2019

The search of conditional outliers

Authors
Portel, E; Ribeire, RP; Gama, J;

Publication
INTELLIGENT DATA ANALYSIS

Abstract
There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On one hand, outliers are considered error measurement observations that should be removed from the analysis, e.g. robust statistics. On the other hand, outliers are the interesting observations, like in fraud detection, and should be modelled by some learning method. In this work, we start from the observation that outliers are affected by the so-called simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a data set, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expect that the deeper nodes of the tree would contain less and less outliers. We observe that some points previously signalled as outliers are no more signalled as such, but new outliers appear.

2019

The search of conditional outliers

Authors
Portela, E; Ribeiro, RP; Gama, J;

Publication
Intell. Data Anal.

Abstract

2019

The search of conditional outliers

Authors
Portela, E; Ribeiro, RP; Gama, J;

Publication
Intelligent Data Analysis

Abstract

2018

Proceedings of the Workshop on Large-scale Learning from Data Streams in Evolving Environments (STREAMEVOLV 2016) co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016), Riva del Garda, Italy, September 23, 2016

Authors
Mouchaweh, MS; Bouchachia, H; Gama, J; Ribeiro, RP;

Publication
STREAMEVOLV@ECML-PKDD

Abstract

Supervised
thesis

2017

Previsão de tempos cirúrgicos num hospital

Author
Sílvia Susana de Moura Carvalho

Institution
UP-FEP

2017

Análise de variação de consumos de instalações não telecontadas

Author
Hélder Manuel Rodrigues Pereira da Costa

Institution
UP-FEP

2017

Previsão de valores de humidade do solo com dados de rede de sensores sem fios (wireless) em área aberta e previsão meteorológica da internet para aplicação na agricultura

Author
André Manuel Marques Ferreira Lino

Institution
UP-FEP

2017

Internship at PwC Belgium: Entity extraction

Author
Tomáš Zdražil

Institution
UP-FEP

2017

Exploratory analisys: The importance of the Ego in a trust network

Author
Marta Pinto Leite Maximiano Ferreira

Institution
UP-FEP