Binary relations-preserving incremental pseudo-equiconcept reduction for symmetric formal context

Expert Systems with Applications. 2025. Vol. 276. Article 127086.

Huilin F., Fei H., Linkai Z., Jin L., Longjiang G., Weihua X., Kuznetsov S., Vincenzo L.

Concept reduct refers to the minimal subset of concepts that preserves the binary relation of the binary data table (formal context). Importantly, it reduces the complexity of problem-solving and improves the efficiency of concept-cognition using formal concept analysis (FCA). Particularly, for a symmetric formal context, there exists a significant class of concept reducts given by equiconcepts. The existing concept reduction algorithms suffer from low efficiency. To this end, this paper proposes an efficient incremental equiconcept-driven concept reduction algorithm. Here we introduce pseudo-equiconcept reduction that preserves the binary relation of the context and present an incremental pseudo-equiconcept reduction algorithm. Extensive experiments demonstrate that the performance of the resulting pseudo-equiconcept reduct is better than that of the concept reduct formed by equiconcepts, in terms of redundancy, average correlation, and running time.

Keywords: Formal Concept Analysis (FCA), Equiconcept-driven concept reduction, Incremental learning, Pseudo-equiconcept reduction, Symmetric formal context

Publication based on the results of: Complex language and semantic models in artificial intelligence (2025)