Research|
Optimise Heterogeneous Ensemble Search
Ensemble Learning has become increasingly prevalent as an efficient paradigm in Machine Learning, combining multiple weak models to produce robust predictions across various fields. A key challenge within Ensemble Learning is ensuring diversity among models to explore different data patterns and maintain heterogeneity. This thesis presents a project focused on optimising the parameter search process for heterogeneous ensemble models that incorporate diverse architectures and tasks.