TY - JFULL AU - Suraiya Jabin and Kamal K. Bharadwaj PY - 2007/12/ TI - Learning Classifier Systems Approach for Automated Discovery of Crisp and Fuzzy Hierarchical Production Rules T2 - International Journal of Computer and Information Engineering SP - 3396 EP - 3402 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/8236 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 11, 2007 N2 - This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system. ER -