Hierarchical Concept Learning by Fuzzy Semantic Cells - Linna Zhu (MDPI AG, 2021)
Concept modeling and learning have been important research topics in artificial intelligence and knowledge discovery. This paper studies a hierarchical concept learning method that requires a small amount of data to achieve competitive performances. The method starts from a set of fuzzy prototypes called Fuzzy Semantic Cells (FSCs). As a result of FSC parameter optimization, it creates a hierarchical structure of data–prototype–concept. Experiments are conducted to demonstrate the effectiveness of our approach in a classification problem. In particular, when faced with limited training data, our proposed method is comparable with traditional techniques in terms of robustness and generalization ability.