EVENTO
Impact of Subdomain Identification on the Efficiency of Machine Learning Models
Tipo de evento: Defesa de Dissertação de Mestrado
This research explores the use of clustering techniques to enhance predictive modeling performance for multivariate time series data. The objective is to determine whether training models on clusters can achieve results that are comparable to or better than a single global model trained on the entire dataset.K-medoids and quadtree-based algorithms were employed to create clusters, utilizing Dynamic Time Warping (DTW) as the dissimilarity measure for both methods. For the quadtree approach, entropy was additionally used as a criterion for partitioning the input space.Long Short-Term Memory (LSTM) networks were employed to train and evaluate models, with performance compared against the global model. This approach provides a robust framework for testing the hypothesis that subset modeling, based on clustered data, can enhance predictive accuracy or maintain comparable performance to the global model, while potentially offering computational or analytical advantages.Evento HíbridoLocal: Auditório BLink de transmissão:meet.google.com/yzm-irbz-amu
Data Início: 18/02/2025 Hora: 14:00 Data Fim: 18/02/2025 Hora: 17:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Gerardo Samuel Rojas Torres - - LNCC
Orientador: Fabio André Machado Porto - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Eduardo Soares Ogasawara - Centro Federal de Educação Tecnológica Celso Suckow da Fonseca - CEFET-RJ Fabio Andre Machado Porto - Laboratório Nacional de Computação Científica - LNCC Marcio Rentes Borges - Laboratório Nacional de Computação Científica - LNCC
Suplente Banca Examinadora: Eduardo Bezerra da Silva - Centro Federal de Educação Tecnológica Celso Suckow da Fonseca - CEFET-RJ Gilson Antônio Giraldi - Laboratório Nacional de Computação Científica - LNCC
