Abstract
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.
Highlights
This work is mainly concerned with concept learning
Many classic machine learning algorithms are related to the prototype theory, such as K-means algorithm, knearest neighbor (KNN) algorithm and so on
Lawry and Tang introduced an approach to uncertainty modeling for vague concepts by combining the prototype theory and the random set theory [10,11]
Summary
This work is mainly concerned with concept learning. Concept learning categorizes the process to partition samples into classes for the purpose of generalization, discrimination, and inference [1]. Concept modeling is fundamental in the fields of cognitive science and artificial intelligence. One prominent work on the cognitive representations of concepts in natural language is the prototype theory [5,6]. The modeling of concept vagueness in artificial intelligence has been dominated by ideas from fuzzy set theory as originally proposed by Zadeh [7,8]. Lawry and Tang introduced an approach to uncertainty modeling for vague concepts by combining the prototype theory and the random set theory [10,11]. Tang and Xiao [15] adopt Fuzzy Semantic Cell (FSC) to name this model. Based on FSC, Tang and Xiao provided an efficient way for unsupervised concept learning [15]
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