This paper proposes the Misclassification Detection and Correction method based on Conditional variational autoencoder (MDC/C) which detects and corrects the incorrect output of Learning Classifier System (LCS) through a comparison between the original data and the restored data by Conditional Variational Auto-Encoder (CVAE) with the output of LCS (as the condition to CVAE).The experimental results on the complex multi-class classification problem of the handwritten numerals have revealed the following implications: (1) although an integration of XCSR (i.e., the real-valued LCS) with CVAE (called CVAEXCSR) increases the correct rate in comparison with XCSR, it has the limit of improvement, i.e., the correct rate converges to 87.92%; (2) the correct rate of CVAEXCSR increases to 99.04% when removing the incorrect outputs by the detection mechanism of MDC/C and 95.03% when correcting them by the correction mechanism of MDC/C, respectively; and (3) the correct rate of CVAEXCSR with MDC/C is high from the first iterations and keeps it high even after the rule condensation which executes LCS without the crossover and mutation operations.
