Abstract
In conventional studies, many methods to generate fuzzy inference rules or neural networks which can compute the input-output relation of the given input-output data were proposed. However, all these methods deal with only numerical attributes which can be naturally expressed in number(e.g. weight, length, etc). In dealing with real world problems, not only numerical attributes but also symbolic attributes which can be naturally expressed by symbols should be handled. In this paper, we propose a new method which can generate appropriate inference rules even if both numerical attributes and symbolic attributes are included in the given input-output data.
