Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System

This paper proposes ELPSDeCS (Encoding, Learning, "Plausible" Sampling, and Decoding Classifier System) by extending ELSDeCS (Encoding, Learning, Sampling, and Decoding Classifier System) to increase both the accuracy and interpretability of the generated classifiers which matches the high-dimensional input such as images. The experimental results on the complex multi-class classification problem of the handwritten numerals show that both the accuracy and interpretability of ELPSDeCS are higher than that of ELSDeCS.