On missing values and fuzzy rules
Michael R. Berthold, Klaus–Peter Huber · 1996
Numerous learning tasks involve incomplete or conflicting attributes. Most algorithms that automatically find a set of fuzzy rules are not well suited to tolerate missing values in the input vector, and the usual technique to substitute missing values by their mean or another constant value can be quite harmful. In this paper a technique is proposed to tolerate missing values during the classification process as well as during training. This is achieved by using the evolving model to predict the most possible value for the missing attribute, resulting in a "best guess" for the feature vector which is then used to further adjust (or train) the set of fuzzy rules. 1 Introduction Most learning algorithms in practical applications have to deal with missing values, due to unrecorded information or occlusions of features, for example in vision applications. Often the missing attribute is simply replaced by its mean. In this paper an approach is presented to use the evolving model to estimat...