Multi-granularity interval-intent fuzzy concept-cognitive learning: An attention-enhanced adaptive clustering framework

Abstract

Cognitive processes lie at the heart of artificial intelligence (AI) research, and the Multi-Granularity Interval-Intent Fuzzy Concept-Cognitive Learning model (MIFCL-A) presented in this paper offers a novel perspective on this domain. MIFCL-A innovatively incorporates multi-level attention mechanism to replicate the intricacies of human cognition, utilizing advanced concept cognitive learning methodologies. This model addresses several limitations inherent in existing concept learning frameworks, such as reliance on manual parameter tuning for concept clustering, the generation of pseudo concepts that compromise cognitive consistency, and an overreliance on attribute-based concept attention that neglects the centrality of objects. Our model introduces a multi-granularity concept structure that captures both global (coarse-granularity) and local (fine-granularity) perspectives, integrating global decision concepts with boundary-derived local concepts. It features a hierarchical attention mechanism that applies global attribute attention at the coarse-granularity level and local concept attention at the fine-granularity level. Moreover, an adaptive concept clustering algorithm is incorporated, which negates the need for manual parameter tuning and ensures the precision and robustness of concept evolution across varying granularities. Comparative evaluations indicate that MIFCL-A outperforms current models in terms of classification accuracy and knowledge representation capabilities, establishing its potential as an effective tool for knowledge discovery and data mining.