원문정보
초록
영어
When we are encountered with a dataset constituting imprecise clusters, usually Neural Networks (NNs) are not sufficient to classify overlapped boundaries of classes. In such a situation, fuzzy processing by which vagueness is handled sufficiently may be utilized to overcome classification difficulties. In the present paper, we use an ensemble of NNs which are trained using different subsets of entire training data set. Then a fuzzy inference unit is used to process the outputs of NNs. A criterion is introduced to modify the topologies of NNs and in addition, fuzzy rules are generated simultaneously and automatically. Also a method is presented to divide the feature space into Regions of Competence (ROC). Each classifier in the ensemble will be an expert for a ROC.
목차
1. Introduction
2. Description of the Proposed System
2.1. Regions of Competence
2.2. Fuzzy Rules
2.3. Classification of an Unknown Pattern
3. Evaluation of the Proposed System
3.1. Artificial Dataset
3.2. Iris Data Classification Problem
3.3. Handwriting Signature Recognition
4. Conclusion
References