Logifold: A Geometrical Foundation of Ensemble Machine Learning

Abstract--We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to form ulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particul ar, this provides a mathematical foundation for ensemble machi ne learning. Our experiments demonstrate that logifolds can b e 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 t he importance of restricting to domains of classifiers in an ens emble.