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概念认知学习是一种新兴的交叉研究热点领域，旨在通过模仿人类的认知过程不断学习新知识。然而，现有的概念认知学习模型通常忽略了概念中对象的局部差异性、概念空间的冗余性、概念可解释性等问题，导致模型认知偏差与有效信息利用不足。因此，提出一种融合隶属度与覆盖的模糊概念认知学习（fuzzy concept-cognitive learning model integrating membership degree and coverage,IMDC）模型。首先，为了提高概念外延的表征能力，引入一种带偏移阈值的隶属度函数探讨对象与概念之间的相关性，并构造隶属度矩阵，进一步将概念空间转化为模糊覆盖；其次，通过模糊β截集筛选高相关对象，结合覆盖率探索不同概念的地位，从而构建核心概念空间，以有效降低概念空间的冗余性，提高认知学习效率；然后，基于线索与核心概念之间的相似性实现概念分类；最后，采用十折交叉验证方法，将提出的模型与4种机器学习算法和2种概念认知算法进行对比。实验结果表明，该模型在14个数据集上的平均精度均高于其他对比算法，并且在不同数据集上的性能波动范围最小，此外，在查准率、查全率、F1值方面也保持领先优势，充分验证了该模型的可行性和有效性。
Abstract：Concept-cognitive learning is an emerging interdisciplinary research area that aims to continuously learn new knowledge by imitating the human cognitive process. However, existing concept-cognitive learning models usually ignore the local variability of objects in concepts, the redundancy of concept space, and the interpretability of concepts, which leads to model cognitive bias and underutilization of valid information. Therefore, a fuzzy concept-cognitive learning model integrating membership degree and coverage is proposed in this paper. Firstly, to enhance the representation capability of the concept extension, a membership function with an offset threshold is introduced to explore the correlation between objects and the concept. A membership matrix is then constructed to further transform the concept space into a fuzzy coverage. Secondly, high-correlation objects are filtered through the fuzzy β cut set, and the importance of different concepts is explored through coverage rates. This enables the construction of a core concept space, which effectively reduces the redundancy of the concept space and enhances cognitive learning efficiency.Finally, the proposed model is compared with four machine learning algorithms and two concept-cognition algorithms using the ten-fold cross validation method. The experimental results demonstrate that the model achieves higher average accuracy than the other comparative algorithms across 14 datasets, with the smallest performance fluctuation range across different datasets. Moreover, it maintains a leading position in terms of precision, recall, and F1 score,fully validating the feasibility and effectiveness of the proposed model.
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基本信息:
DOI：10.12194/j.ntu.20250606001
中图分类号:TP18
引用信息:
[1]吴雨青,林艺东,梁涛巨.一种融合模糊覆盖的模糊概念认知学习[J].南通大学学报(自然科学版),2025,24(03):23-33+51.DOI:10.12194/j.ntu.20250606001.
基金信息:
国家自然科学基金青年科学基金项目(12201284); 福建省自然科学基金项目(2022J05169)