Bayesian Hyperparameter Optimization for Ensemble Learning
This work addresses hyperparameter tuning for ensemble methods, which is an incremental improvement in machine learning optimization.
The paper tackles the problem of optimizing hyperparameters for ensemble learning by introducing a Bayesian optimization method that considers interactions between ensemble members, resulting in improved performance over single models and standard greedy ensemble construction.
In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of an ensemble with regards to its hyperparameters. Our method consists in building a fixed-size ensemble, optimizing the configuration of one classifier of the ensemble at each iteration of the hyperparameter optimization algorithm, taking into consideration the interaction with the other models when evaluating potential performances. We also consider the case where the ensemble is to be reconstructed at the end of the hyperparameter optimization phase, through a greedy selection over the pool of models generated during the optimization. We study the performance of our proposed method on three different hyperparameter spaces, showing that our approach is better than both the best single model and a greedy ensemble construction over the models produced by a standard Bayesian optimization.