EVENTO
Stream Ensemble: A Machine Learning Model Selection Algorithm for Stream Data
Tipo de evento: Defesa de Tese de Doutorado
Predictive queries over spatiotemporal (ST) stream data present substantial challenges in data processing andanalysis. ST data streams encompass a series of time-dependent data distributions that vary across both space and time, often displaying distinct and dynamic patterns. Relying on a single machine learning model designed for a specific data distribution frequently leads to suboptimal outcomes, as such a model is unlikely to capture the diverse behaviors present in different spatiotemporal regions. Traditional ensemble methods, which aim to leverage the complementary strengths of multiple base models, tend to suffer from high computational costs and subpar performance when applied to ST data due to the complexity of integrating each models contributions effectively. Likewise, global modelstrained on comprehensive datasetsare often inadequate, facing several challenges such as insufficient data, higher complexity, and the inefficiency of retraining when more specialized models are already available. To address these limitations, we propose an approach that optimizes predictive accuracy by considering both the training data and generalization errors of available models, as well as the target data distribution. For each time series, our method selects the most suitable model. Based on these principles, we developed StreamEnsemble, na innovative method for processing predictive queries over ST data that dynamically combines multiple candidate models. Experimental results demonstrate that StreamEnsemble significantly outperforms both traditional ensemble techniques and single-model approaches, reducing prediction error by more than tenfold while achieving faster execution times.Evento HíbridoLocal: Auditório BLink de Transmissão: meet.google.com/huu-xgja-htb
Data Início: 28/11/2024 Hora: 09:00 Data Fim: 28/11/2024 Hora: 12:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Anderson Chaves da Silva - - LNCC
Orientador: Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Antônio Tadeu Azevedo Gomes - Laboratório Nacional de Computação Científica - LNCC Daniel Cardoso Moraes de Oliveira - Universidade Federal Fluminense - UFF Eduardo Soares Ogasawara - Centro Federal de Educação Tecnológica Celso Suckow da Fonseca - CEFET / RJ Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC Patrick Valduriez - INRIA - FRA
Suplente Banca Examinadora: Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC
