Knowledge acquisition based on learning of maximal structure fuzzy rules

There are a lot of systems that make decisions or classifications on the basis of a number of rules. This set of rules that govern such a system is called the rule base. When a new system of this kind is being developed, setting up its rule base is a time-consuming and expensive process because the rule base contains the knowledge of the outside world, which could be acquired from experts or produced from previous experiences. In this latter case, machine-learning algorithms can help. In fact, many methods have been proposed to generate rules from training instances. The aim of this paper is to present a new fuzzy learning algorithm to generate IF-THEN rules, for classifying instances in one application domain. This algorithm can improve the results offered by a previously presented algorithm. In addition, the more common classification problems of the original algorithm are presented and a measure to determine the conflicts among generated rules is introduced. Moreover, we study the classification stage of that inductive fuzzy learning algorithm and an improvement is suggested to obtain better classification results.