A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length

TL;DR

This paper introduces MDL-GBC, a boundary-aware, interpretable granular-ball classifier that uses the Minimum Description Length principle for local model selection, improving transparency and performance.

Contribution

It proposes a novel MDL-based approach for granular-ball construction, explicitly modeling boundary-sensitive regions and enhancing interpretability over heuristic methods.

Findings

Achieves the best average Accuracy, Macro-F1, and rank on 18 benchmark datasets.

Provides a boundary-aware, interpretable classification mechanism.

Outperforms classical classifiers and existing granular-ball methods.

Abstract

Existing granular-ball classification methods are often driven by handcrafted quality measures, neighborhood rules, or heuristic splitting and stopping criteria, which may reduce the transparency of local construction decisions and hinder explicit modeling of boundary-sensitive regions. To address this issue, this paper proposes a Minimum Description Length based Granular-Ball Classifier (MDL-GBC), a boundary-aware non-parametric and interpretable granular-ball classifier. MDL-GBC formulates class-conditional granular-ball construction as a local model selection problem under the Minimum Description Length principle. For each class, samples from the target class provide positive class evidence, while samples from the remaining classes provide negative boundary evidence. For each current granular ball, three candidate explanations are compared under a unified description-length criterion: a single-ball model, a two-ball model, and a core-boundary model. The selected model determines whether the ball is retained, geometrically split, or refined into core and boundary-sensitive child balls, thereby making local construction decisions consistent with the MDL-based classification mechanism. During prediction, a class-level mixture coding rule aggregates stable granular balls of the same class and assigns the test sample by comparing class-wise coding costs. Experiments on 18 benchmark datasets show that MDL-GBC achieves competitive classification performance against classical classifiers and representative granular-ball-based methods, obtaining the best average Accuracy, Macro-F1, and average rank. These results indicate that MDL-GBC provides an effective and interpretable alternative to conventional heuristic granular-ball classification strategies.