Concept lattice model, the core structure in formal concept analysis, has been successfully applied in software engineering and knowledge discovery. This paper integrates the simple base classifier (Na ı¨ve Bayes or Nearest Neighbour) into each node of the concept lattice to form a new composite classifier. Two new classification systems are developed, CLNB and CLNN, which employ efficient constraints to search for interesting patterns and voting strategy to classify a new object. CLNB integrates the Na ı¨ ı¨ve Bayes base classifier into concept nodes while CLNN incorporates the Nearest Neighbour 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-the-art classification methods, NBTree, CBA and C4.5 Rules.