Incremental cognitive learning approach based on concept reduction

Abstract

Concept-cognitive learning (CCL) offers an innovative approach to classification, and concept reduction serves as a powerful method for compressing data. Nonetheless, most existing CCLs encounter a significant issue when attempting to downscale the concept space: information loss. This loss leads to cognitive incompleteness and increased complexity. Meanwhile, preserving the native characterization of formal concepts ensures both validity and interpretability for CCL. On the other hand, current incremental CCLs have limited capacity to effectively utilize newly acquired knowledge. In view of these observations, in this article, we propose a novel incremental CCL method based on concept reduction for dynamic classification. To enhance the efficiency of knowledge acquisition, recovery degree is developed to obtain concept reduction from granular concept space. Subsequently, the updating mechanism for concept reduction is explored in dynamic environments. For label recognition, a learning method based on concept reduction is discussed and an incremental learning mechanism for dynamic increased data is further constructed. Empirical studies on fifteen datasets reveal the feasibility and effectiveness of proposed model.