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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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

Received: 27 July 2022

ZTFLH:

TP 182

Fund:

Supported by National Natural Science Foundation of China(No.12201284), Natural Science Foundation of Fujian Province(No.2022J05169,B12127), President Foundation of Minnan Normal University(No.L22130)

Corresponding Authors: LIN Menglei, master, professor. His research interests include uncertain theories and application.

About author:: WANG Qijun, master student. Her research interests include granular computing and concept-cognitive learning. LIN Yidong, Ph.D., associate professor. His research interests include uncertain theories and application. KOU Yi, master student. His research interests include granular computing and machine learning.

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Cite this article:

WANG Qijun,LIN Yidong,LIN Menglei等. Concept-Cognitive Learning Model Based on Decision Significance[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(9): 816-826.

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

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