Granular ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method. (arXiv:2304.11171v1 [cs.LG])

Human cognition has a “large-scale first” cognitive mechanism, therefore
possesses adaptive multi-granularity description capabilities. This results in
computational characteristics such as efficiency, robustness, and
interpretability. Although most existing artificial intelligence learning
methods have certain multi-granularity features, they do not fully align with
the “large-scale first” cognitive mechanism. Multi-granularity granular-ball
computing is an important model method developed in recent years. This method
can use granular-balls of different sizes to adaptively represent and cover the
sample space, and perform learning based on granular-balls. Since the number of
coarse-grained “granular-ball” is smaller than the number of sample points,
granular-ball computing is more efficient; the coarse-grained characteristics
of granular-balls are less likely to be affected by fine-grained sample points,
making them more robust; the multi-granularity structure of granular-balls can
produce topological structures and coarse-grained descriptions, providing
natural interpretability. Granular-ball computing has now been effectively
extended to various fields of artificial intelligence, developing theoretical
methods such as granular-ball classifiers, granular-ball clustering methods,
granular-ball neural networks, granular-ball rough sets, and granular-ball
evolutionary computation, significantly improving the efficiency, noise
robustness, and interpretability of existing methods. It has good innovation,
practicality, and development potential. This article provides a systematic
introduction to these methods and analyzes the main problems currently faced by
granular-ball computing, discussing both the primary applicable scenarios for
granular-ball computing and offering references and suggestions for future
researchers to improve this theory.