Weighted Attributes-Based Concept-Cognitive Learning Model
LIANG Taoju1,2,3, LIN Yidong1,2,3, LIN Menglei1,2,3, WANG Qijun1,2,3
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. Institute of Meteorological Big Data-Digital Fujian, Minnan Normal University, Zhangzhou 363000; 3. Fujian Key Laboratory of Granular Computing and Application, Minnan Normal University, Zhangzhou 363000
Abstract In the existing concept-cognitive learning models, correlations among attributes and decisions are ignored, the concept space involved is redundant and the learning effectiveness is poor. Aiming at these problems, a weighted attributes-based concept-cognitive learning model(WACCL) is proposed in this paper. Firstly, the relevance of attribute-decision is discussed, and a weighting mechanism for attributes is proposed. With the consideration of the concept space redundancy, the positions of different concepts are explored and the compression of the concept space is achieved. Subsequently, conceptual clustering is implemented by combining similarities between concepts to provide a basis for learning clues. Finally, the effectiveness of WACCL is demonstrated by experiments on 13 datasets.
Fund:National Natural Science Foundation of China(No.12201284), Natural Science Foundation of Fujian Province(No.2022J05169,JAT210255), President Foundation of Minnan Normal University(No.L22130)
Corresponding Authors:
LIN Yidong, Ph.D., associate professor. His research interests include uncertain theories and application.
About author:: LIANG Taoju, master student. His research interests include granular computing and concept-cognitive learning.LIN Menglei, master, professor. His research interests include uncertain theories and application.WANG Qijun, master student. Her research interests include granular computing and concept-cognitive learning.
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2023, Vol. 36 
