AI RESEARCH
A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
arXiv CS.LG
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ArXi:2605.11406v1 Announce Type: new 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.