FILSMR: a fuzzy inductive learning strategy for modular rules

In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in linguistic environments is thus very important. We apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. Experiments on the sport classification problem are to demonstrate the effectiveness of the proposed algorithm. Experimental results show that the rules derived from our approach are simpler and yields high accuracy