Human Cognition-Inspired Hierarchical Fuzzy Learning Machine

Junbiao Cui, Qin Yue, Jianqing Liang, Jiye Liang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:11620-11648, 2025.

Abstract

Classification is a cornerstone of machine learning research. Most of the existing classifiers assume that the concepts corresponding to classes can be precisely defined. This notion diverges from the widely accepted understanding in cognitive science, which posits that real-world concepts are often inherently ambiguous. To bridge this big gap, we propose a Human Cognition-Inspired Hierarchical Fuzzy Learning Machine (HC-HFLM), which leverages a novel hierarchical alignment loss to integrate rich class knowledge from human knowledge system into learning process. We further theoretically prove that minimizing this loss can align the hierarchical structure derived from data with those contained in class knowledge, resulting in clear semantics and high interpretability. Systematic experiments verify that the proposed method can achieve significant gains in interpretability and generalization performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-cui25f, title = {Human Cognition-Inspired Hierarchical Fuzzy Learning Machine}, author = {Cui, Junbiao and Yue, Qin and Liang, Jianqing and Liang, Jiye}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {11620--11648}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/cui25f/cui25f.pdf}, url = {https://proceedings.mlr.press/v267/cui25f.html}, abstract = {Classification is a cornerstone of machine learning research. Most of the existing classifiers assume that the concepts corresponding to classes can be precisely defined. This notion diverges from the widely accepted understanding in cognitive science, which posits that real-world concepts are often inherently ambiguous. To bridge this big gap, we propose a Human Cognition-Inspired Hierarchical Fuzzy Learning Machine (HC-HFLM), which leverages a novel hierarchical alignment loss to integrate rich class knowledge from human knowledge system into learning process. We further theoretically prove that minimizing this loss can align the hierarchical structure derived from data with those contained in class knowledge, resulting in clear semantics and high interpretability. Systematic experiments verify that the proposed method can achieve significant gains in interpretability and generalization performance.} }
Endnote
%0 Conference Paper %T Human Cognition-Inspired Hierarchical Fuzzy Learning Machine %A Junbiao Cui %A Qin Yue %A Jianqing Liang %A Jiye Liang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-cui25f %I PMLR %P 11620--11648 %U https://proceedings.mlr.press/v267/cui25f.html %V 267 %X Classification is a cornerstone of machine learning research. Most of the existing classifiers assume that the concepts corresponding to classes can be precisely defined. This notion diverges from the widely accepted understanding in cognitive science, which posits that real-world concepts are often inherently ambiguous. To bridge this big gap, we propose a Human Cognition-Inspired Hierarchical Fuzzy Learning Machine (HC-HFLM), which leverages a novel hierarchical alignment loss to integrate rich class knowledge from human knowledge system into learning process. We further theoretically prove that minimizing this loss can align the hierarchical structure derived from data with those contained in class knowledge, resulting in clear semantics and high interpretability. Systematic experiments verify that the proposed method can achieve significant gains in interpretability and generalization performance.
APA
Cui, J., Yue, Q., Liang, J. & Liang, J.. (2025). Human Cognition-Inspired Hierarchical Fuzzy Learning Machine. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:11620-11648 Available from https://proceedings.mlr.press/v267/cui25f.html.

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