Towards general measures of comparison of objects
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Classification of Text Documents
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A neural evolutionary approach to financial modeling
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Hierarchical Bayesian clustering for automatic text classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The set of fuzzy rational numbers and flexible querying
Fuzzy Sets and Systems
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Approaches to document classification belong to two major families: similarity-based (crisp) classification methods and neural networks (gradual) ones. For gradual techniques, a major open issue is controlling search space dimension. While similarity-based methods identify clusters based on the same number of variables used for document encoding, neural networks automatically identify variables that cause distinctions among clusters. Therefore, the variables' number may vary depending on the documents structure and content, and is difficult to estimate it a priori. This paper proposes a hybrid classification method suitable for heterogeneous document bases like the ones commonly encountered in business and knowledge management applications. Our method is based on an evolutionary algorithm for tuning both neural network's structure and weights. While searching the optimal neural network's configuration it is possible to determine the minimal number of variables to be used in order to classify the given set of documents.