Missing Values and Learning of Fuzzy Rules

In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classi cation The presented method is mathematically solid but never theless easy and e cient to implement Three possible applications of this methodology are outlined the classi cation of patterns with an incomplete feature vector the com pletion of the input vector when a certain class is desired and the training or automatic construction of a fuzzy rule set based on incomplete training data In contrast to a static replacement of the missing values here the evolving model is used to predict the most possible values for the missing attributes Benchmark datasets are used to demonstrate the capability of the presented approach in a fuzzy learning environment