基于决策重要度的概念认知学习模型

doi:10.16451/j.cnki.issn1003-6059.202209005

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摘要

概念认知学习是模拟人类认知过程的概念学习方法,大多从概念相似性的角度出发,往往忽略先验决策信息的影响,造成有效信息的丢失. 因此,文中提取先验决策信息, 刻画决策重要性,提出基于决策重要度的概念认知学习模型,用于动态环境中的概念归类.首先,通过余弦相似度构造邻域粒,探讨概念认知的渐进过程.然后,面对动态环境,考虑到先验决策信息对后续学习的影响,进一步提出决策重要度和置信度,设计概念分类的计算方法.最后,通过仿真实验验证文中模型的有效性和优越性.

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	王启君
	林艺东
	林梦雷
	寇毅

关键词 ：概念认知学习, 模糊概念, 决策重要度, 渐进模糊概念

Abstract：

Concept-cognitive learning is a concept learning method that simulates human cognitive process based on formal concept analysis. Most of the current concept-cognitive learning methods only consider conceptual similarity and ignore the influence of prior decision information, resulting in the loss of practical details. To solve this problem, a concept-cognitive learning model based on decision significance is put forward for concept classification in a dynamic environment by extracting prior decision information to describe the significance of decision making. The neighborhood granule is constructed by cosine similarity, and the progressive process of concept cognition is discussed. For the dynamic environment, the decision significance and confidence degree are proposed to design the computational method of concept classification with the consideration of the validity of the a priori decision information. The effectiveness and superiority of the proposed method are verified by simulation experiments.

Key words： Concept-Cognitive Learning Fuzzy Concept Decision Significance Progressive Fuzzy Concept

收稿日期: 2022-07-27

ZTFLH:

TP 182

基金资助:

国家自然科学基金项目(No.12201284)、福建省自然科学基金项目(No.2022J05169,B12127)、闽南师范大学校长基金项目(No.L22130) 资助

通讯作者: 林梦雷,硕士,教授,主要研究方向为不确定性理论及其应用.E-mail:menglei36@126.com.

作者简介: 王启君,硕士研究生,主要研究方向为粒计算、概念认知学习.E-mail:wqj_1106@163.com. 林艺东,博士,副教授,主要研究方向为不确定性理论及其应用.E-mail: yidong_lin@yeah.net. 寇毅,硕士研究生,主要研究方向为粒计算、机器学习.E-mail:ky20eku@163.com.

引用本文:

王启君, 林艺东, 林梦雷, 寇毅. 基于决策重要度的概念认知学习模型[J]. 模式识别与人工智能, 2022, 35(9): 816-826. WANG Qijun, LIN Yidong, LIN Menglei, KOU Yi. Concept-Cognitive Learning Model Based on Decision Significance. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 816-826.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202209005 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2022/V35/I9/816

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