Company MISSING VALUES AND LEARNING OF FUZZY

Received (received date) Revised (revised date) In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classiication. The presented method is mathematically solid but nevertheless easy and eecient to implement. Three possible applications of this methodology are outlined: the classiication of patterns with an incomplete feature vector, the completion 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.