Using Genetic Fuzzy Algorithms to Model the Evolution of Climate Variables at San Jorge Gulf Area
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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With the immense increase of the data in various fields, interpreting the data into useful information has become a tedious job. Design of models to handle the problem is essential. This paper discusses the methods that handle uncertain information with continuous data and deliver comprehensible classification model. We investigate fuzzy decision tree as a method for classification problems and axiomatic fuzzy set for building fuzzy sets (membership functions) . To select the best available test attributes of fuzzy decision trees we use a generalized Shannon Entropy. The problems connected with this generalization arised from fuzzy domain are discussed and some alternatives are proposed.