Association-based concept-cognitive learning for classification: Fusing knowledge with distance metric learning

Abstract

Concept-cognitive learning, which emphasizes the representation and learning of knowledge incorporated within data, has yielded excellent results in classification research. However, learning concepts from a high-dimensional dataset is a time-consuming and complex process, which increases the extraction of redundant information and leads to poor classification task. Most existing neighborhood concept generated by neighborhood similarity granule use a single predefined distance function and ignore the decision labels, which lead to the fact that the learned distance function is not optimal. Moreover, current concept-cognitive learning methods do not fully utilize the advantages of granular concept and neighborhood concept, resulting in weak interpretability. To address these issues, we introduce a novel association-based concept-cognitive learning method with distance metric learning for knowledge fusion and concept classification. To be concrete, to decrease the dimensionality of dataset and remove the interfering information, the representative attribute set from attribute clusters based on correlation coefficient matrix is firstly discussed. Subsequently, neighborhood similarity granules based on distance metric learning are used to construct fuzzy concepts. To obtain fuzzy concept of maximum contribution, we present a valid fuzzy concept associative space related to clues in the human brain. Furthermore, a mechanism of fuzzy concept-cognitive associative learning with distance metric learning (FCADML) model is proposed, which aims to achieve concept clustering and class prediction by fusing objects and attributes within fuzzy concepts. Finally, we perform a classification performance evaluation on thirteen datasets which verify that the feasibility and efficiency of the proposed learning mechanism.