Mining of kernel concepts with optimization of concept set generation costs

Abstract This study introduces a novel concept lattice reduction model that integrates heuristic and machine learning optimization approaches to balance expressiveness with computational efficiency. The resulting set of concepts is considered the kernel of the original context. The proposed kernel induction method applies an optimized genetic algorithm with machine learning support for kernel selection. In the efficiency comparison tests, we also analyzed the simulated annealing method, the particle swarm optimization method, and a derivative-free, ranking-based optimizer method. Experimental evaluations on synthetic and real-world datasets reveal that the proposed genetic algorithm variant outperforms the other benchmark methods in both computational efficiency and effectiveness while maintaining scalability. The applicability of the proposed method is demonstrated through a linguistic-domain case study on selecting an optimal kernel vocabulary.