On Missing Values and Fuzzy Rules
نویسنده
چکیده
Numerous learning tasks involve incomplete or connicting attributes. Most algorithms that automatically nd 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 classiication 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.
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