Missing Values in Fuzzy Rule Induction

In this paper, we show how an existing fuzzy rule induction algorithm can incorporate missing values in the training procedure in a very natural way. The underlying algorithm generates rules which restrict the feature space only along a few, important attributes. This property can be used to limit the algorithm´s three major steps to the reduced feature space for each training instance, which allows the features for which no values are known to be ignored. Hence no replacement is necessary and the algorithm simply uses all available knowledge from each training instance. We demonstrate on data sets from the UCI repository that this method works well, generates rule sets that have comparable classification accuracy, and are, at times, even smaller than the rule sets generated by the original algorithm