  Ensemble learning has been a focal point of machine learning research due to
its potential to improve predictive performance. This study revisits the
foundational work on ensemble error decomposition, historically confined to
bias-variance-covariance analysis for regression problems since the 1990s.
Recent advancements introduced a "unified theory of diversity," which proposes
an innovative bias-variance-diversity decomposition framework. Leveraging this
contemporary understanding, our research systematically explores the
application of this decomposition to guide the creation of new ensemble
learning algorithms. Focusing on regression tasks, we employ neural networks as
base learners to investigate the practical implications of this theoretical
framework. This approach used 7 simple ensemble methods, we name them
strategies, for neural networks that were used to generate 21 new ensemble
algorithms. Among these, most of the methods aggregated with the snapshot
strategy, one of the 7 strategies used, showcase superior predictive
performance across diverse datasets w.r.t. the Friedman rank test with the
Conover post-hoc test. Our systematic design approach contributes a suite of
effective new algorithms and establishes a structured pathway for future
ensemble learning algorithm development.
