This paper focuses on Identification-based XCS (IXCS) which introduces the identification mechanism into XCS (Accuracy-based Leraning Classifier System) and extends it to Predicted reward-based IXCS(PIXCS) to promote a generalization of classifiers(i.e., rules) in the binary and multi-classification problems with reducing the number of classifiers. Through the intensive simulation of 20-Multiplexer problem and 3×3 Concatenated multiplexer problem, this paper has revealed the following implications which cannot be achieved by the conventional LCS(i.e., XCSTS) and IXCS: (1) PIXCS can derive better performance than XCSTS and IXCS in the binary-classification problem, (2) PIXCS can generalize not only the classifiers faster than IXCS but also the classifiers which are robust in the noisy multi-classification problem with reducing the number of classifiers.
