Concept-Cognitive Learning Model Based on Decision Significance

被引：0
|
作者：
Wang Q. [1 ]
Lin Y. [1 ]
Lin M. [1 ,2 ]
Kou Y. [1 ]
机构：
[1] School of Mathematics and Statistics, Minnan Normal University, Zhangzhou
[2] Institute of Meteorological Big Data-Digital Fujian, Minnan Normal University, Zhangzhou
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2022年 / 35卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Concept-Cognitive Learning; Decision Significance; Fuzzy Concept; Progressive Fuzzy Concept;
D O I：
10.16451/j.cnki.issn1003-6059.202209005
中图分类号：
学科分类号：
摘要：
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. © 2022 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.
引用
收藏
页码：816 / 826
页数：10
相关论文
共 31 条
  • [1] LIN B Y, ZHANG X Y, XU W H, Et al., Dynamically Updating Approximations Based on Multi-threshold Tolerance Relation in Incomplete Interval-Valued Decision Information Systems, Knowledge and Information Systems, 62, 3, pp. 1063-1087, (2020)
  • [2] MI Y L, SHI Y, LI J H, Et al., Fuzzy-Based Concept Learning Method: Exploiting Data with Fuzzy Conceptual Clustering, IEEE Transactions on Cybernetics, 52, 1, pp. 582-593, (2020)
  • [3] TSANG E C C, FAN B J, CHEN D G, Et al., Multi-level Cognitive Concept Learning Method Oriented to Data Sets with Fuzziness: A Perspective from Features, Soft Computing, 24, 5, pp. 3753-3770, (2020)
  • [4] ZHANG Q, SHI C Y, NIU Z D, Et al., HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering, IEEE Transactions on Knowledge and Data Engineering, 31, 1, pp. 152-165, (2018)
  • [5] ZOU C F, DENG H F, WAN J F, Et al., Mining and Updating Association Rules Based on Fuzzy Concept Lattice, Future Generation Computer Systems, 82, pp. 698-706, (2018)
  • [6] WILLE R., Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts, Ordered Sets, pp. 445-470, (1982)
  • [7] GANTER B, WILLE R, FRANZKE C., Formal Concept Analysis: Mathematical Foundations, (1999)
  • [8] LI J H, MEI C L, XU W H, Et al., Concept Learning via Granular Computing: A Cognitive Viewpoint, Information Sciences, 298, pp. 447-467, (2015)
  • [9] LONG B H, XU W H, ZHANG X Y, Et al., The Dynamic Update Method of Attribute-Induced Three-Way Granular Concept in Formal Contexts, International Journal of Approximate Reasoning, 126, pp. 228-248, (2020)
  • [10] WEI L, LIU L, QI J J, Et al., Rules Acquisition of Formal Decision Contexts Based on Three-Way Concept Lattices, Information Sciences, 516, pp. 529-544, (2020)
1234