Concept lattice based composite classifiers for high predictability

Concept lattice model, the core structure in Formal Concept Analysis, has been successfully applied in software engineering and knowledge discovery. In this paper, we integrate the simple base classifier (Naïve Bayes or Nearest Neighbor) into each node of the concept lattice to form a new composite classifier. We develop two new classification systems, CLNB and CLNN, that employ efficient constraints to search for interesting patterns and voting strategy to classify a new object. CLNB integrates the Naïve Bayes base classifier into concept nodes while CLNN incorporates the Nearest Neighbor base classifier into concept nodes. Experimental results indicate that these two composite classifiers greatly improve the accuracy of their corresponding base classifier. In addition, CLNB even outperforms three other state-of-art classification methods, NBTree, CBA and C4.5 Rules.