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Sobre

Sobre

João Gama é Professor Associado da Faculdade de Economia da Universidade do Porto. É investigador e vice-diretor do LIAAD, INESC TEC. Concluiu o doutoramento na Universidade do Porto, em 2000. É Sénior member do IEEE. Trabalhou em vários projetos nacionais e europeus sobre sistemas de aprendizagem incremental e adaptativo, descoberta de conhecimento em tempo real, e aprendizagem de dados massivos e estruturados. Foi PC chair no ECML2005, DS2009, ADMA2009, IDA '2011 e ECML / PKDD'2015. Foi track chair ACM SAC de 2007 a 2018. Organizou uma série de Workshops sobre Descoberta de Conhecimento de fluxos de dados no ECMLPKDD, ICML, e no ACM SIGKDD. É autor de vários livros em Data Mining e autoria de uma monografia sobre Descoberta de Conhecimento a partir de fluxos de Dados. É autor de mais de 250 papéis peer-reviewed em áreas relacionadas com a aprendizagem automática, aprendizagem de dados em tempo real e fluxos de dados. É membro do conselho editorial de revistas internacionais ML, DMKD, TKDE, IDA, NGC e KAIS. Supervisionou mais de 15 estudantes de doutoramento e 50 alunos de mestrado.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Gama
  • Cluster

    Informática
  • Cargo

    Investigador Coordenador
  • Desde

    01 abril 2009
012
Publicações

2019

The search of conditional outliers

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

Publicação
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

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

Publicação
Intell. Data Anal.

Abstract

2019

The search of conditional outliers

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

Publicação
Intelligent Data Analysis

Abstract

2019

Development and Field Demonstration of a Gamified Residential Demand Management Platform Compatible with Smart Meters and Building Automation Systems

Autores
Zehir, MA; Ortac, KB; Gul, H; Batman, A; Aydin, Z; Portela, JC; Soares, FJ; Bagriyanik, M; Kucuk, U; Ozdemir, A;

Publicação
ENERGIES

Abstract
Demand management is becoming an indispensable part of grid operation with its potential to aid supply/demand balancing, reduce peaks, mitigate congestions and improve voltage profiles in the grid. Effective deployments require a huge number of reliable participators who are aware of the flexibilities of their devices and who continuously seek to achieve savings and earnings. In such applications, smart meters can ease consumption behavior visibility, while building automation systems can enable the remote and automated control of flexible loads. Moreover, gamification techniques can be used to motivate and direct customers, evaluate their performance, and improve their awareness and knowledge in the long term. This study focuses on the design and field demonstration of a flexible device-oriented, smart meter and building automation system (BAS) compatible with a gamified load management (LM) platform for residential customers. The system is designed, based on exploratory surveys and systematic gamification approaches, to motivate the customers to reduce their peak period consumption and overall energy consumption through competing or collaborating with others, and improving upon their past performance. This paper presents the design, development and implementation stages, together with the result analysis of an eight month field demonstration in four houses with different user types in Istanbul, Turkey.

2019

The search of conditional outliers

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

Publicação
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.

Teses
supervisionadas

2018

Optimal Participation of an Aggregator of Prosumers in the Electricity Markets

Autor
José Pedro Barreira Iria

Instituição
UP-FEUP

2017

Previsão na Indústria Química

Autor
Vânia Cristina Lourenço Pinheiro

Instituição
UP-FEP

2017

Previsão da Taxa de Rebate de Cupões Promocionais em Marketing Direto

Autor
Paulo César Teixeira Granja

Instituição
UP-FEP

2017

Big Data techniques for Solar Power Forecasting

Autor
Rui Miguel da Cunha Nunes

Instituição
UP-FEP

2017

Massive Scale Streaming Graphs: Evolutionary Network Analysis and Mining

Autor
Shazia Tabassum

Instituição
UP-FEP