BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation.

Overview

1. School of Information Engineering, Zhejiang Ocean University, Zhoushan, China
2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
3. School of Mathematical Sciences, Xiamen University, Xiamen, China

Abstract

Drawing inspiration from the brain's neurocognitive mechanisms of information chunking and topographic mapping, adaptive decision-making requires neural-grounded architectures that are interpretable and resilient to uncertainty. In this paper, we propose a novel boosted fuzzy manifold granule hypersurface classifier (BFMGHC). The algorithm performs classification at the “information granule" level, realizing an intelligent modeling method that is closer to human cognition, more interpretable, and more accommodating of uncertainty. The classifier mainly consists of three main parts: (1) A manifold-based measurement method for samples that preserves local topological structure is designed, echoing the topographic representations in neural dynamics. Based on this, a global optimization clustering algorithm is proposed and integrated with the Dask framework to achieve scalable hierarchical parallel granulation from raw inputs to high-level semantic granules. (2) In the fuzzy manifold granule space, a measurement method and a hypersurface classifier are constructed, utilizing a particle swarm optimization method for parameter solving. (3) To improve interpretability, weights are assigned to different granules and base classifiers, resembling bio-inspired neuromodulation to ensure stable behavior. The proposed BFMGHC was verified on three financial risk assessment datasets in the UCI Machine Learning Repository (Default of Credit Card Clients, Bank Marketing, and German Credit Data) and achieved superior performance.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

Datasets cited

• archive.ics.uci.edu/dataset/144, at archive.ics.uci.edu; found in “Data availability statement”
• archive.ics.uci.edu/datasets, at archive.ics.uci.edu; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://archive.ics.uci.edu/datasets/?search=Default+of+Credit+Card+Clients+, https://archive.ics.uci.edu/datasets/?search=Bank+Marketing, and https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Funding: added Ministry of Education of the People's Republic of China: 23YJAZH067

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 48 references.

Cite

This paper

Li, W., Si, W., Liu, Z., & Song, Y. (2026). BFMGHC: a boosted fuzzy manifold granule hypersurface classifier with local topology preservation. Frontiers in neurorobotics, 20, 1899676. https://doi.org/10.3389/fnbot.2026.1899676

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