Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method

TL;DR

This paper introduces granular-ball computing, a multi-granularity method inspired by human cognition that enhances efficiency, robustness, and interpretability in AI by adaptively representing data with variable-sized granular-balls.

Contribution

It presents a novel theoretical framework for multi-granularity data representation using granular-balls, improving upon traditional single-granularity methods in AI applications.

Findings

Improves efficiency by reducing the number of coarse-grained representations.

Enhances robustness against sample disturbances due to coarse-grained nature.

Augments interpretability through topological structures and coarse descriptions.

Abstract

Human cognition operates on a "Global-first" cognitive mechanism, prioritizing information processing based on coarse-grained details. This mechanism inherently possesses an adaptive multi-granularity description capacity, resulting in computational traits such as efficiency, robustness, and interpretability. The analysis pattern reliance on the finest granularity and single-granularity makes most existing computational methods less efficient, robust, and interpretable, which is an important reason for the current lack of interpretability in neural networks. Multi-granularity granular-ball computing employs granular-balls of varying sizes to daptively represent and envelop the sample space, facilitating learning based on these granular-balls. Given that the number of coarse-grained "granular-balls" is fewer than sample points, granular-ball computing proves more efficient. Moreover, the inherent coarse-grained nature of granular-balls reduces susceptibility to fine-grained sample disturbances, enhancing robustness. The multi-granularity construct of granular-balls generates topological structures and coarse-grained descriptions, naturally augmenting interpretability. Granular-ball computing has successfully ventured into diverse AI domains, fostering the development of innovative theoretical methods, including granular-ball classifiers, clustering techniques, neural networks, rough sets, and evolutionary computing. This has notably ameliorated the efficiency, noise robustness, and interpretability of traditional methods. Overall, granular-ball computing is a rare and innovative theoretical approach in AI that can adaptively and simultaneously enhance efficiency, robustness, and interpretability. This article delves into the main application landscapes for granular-ball computing, aiming to equip future researchers with references and insights to refine and expand this promising theory.