An Inductive Learning Strategy with Fuzzy Sets

Abstract

In real applications, data provided to a learning system usually contain noisy and fuzzy information which greatly influences concept descriptions derived by conventional inductive learning methods. Modifying learning methods to learn concept descriptions in noisy and vague environments is thus very important. In this paper, we apply fuzzy set concept to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR strategy is proposed to manage noisy and fuzzy information. The proposed algorithm generates fuzzy linguistic rules from fuzzy instances. In the experiment, the Iris Flower classification problem is used to compare the accuracy of the proposed algorithm with that of some other learning algorithms. Experimental results show that our method yields high accuracy.