Logifold: A Geometrical Foundation of Ensemble Machine Learning
This offers a foundational framework for ensemble learning, potentially benefiting researchers in machine learning theory, though it appears incremental in building on existing ensemble concepts.
The paper tackles the problem of providing a mathematical foundation for ensemble machine learning by introducing a logifold structure to interpret datasets geometrically, and experiments show it improves accuracy compared to averaging model outputs.
We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to formulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particular, this provides a mathematical foundation for ensemble machine learning. Our experiments demonstrate that logifolds can be implemented to identify fuzzy domains and improve accuracy compared to taking average of model outputs. Additionally, we provide a theoretical example of a logifold, highlighting the importance of restricting to domains of classifiers in an ensemble.