Missing Values and Learning of Fuzzy Rules

Abstract

In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classification. The presented method is mathematically solid but nevertheless easy and efficient to implement. Three possible applications of this methodology are outlined: the classification 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.