a refinement model with information granulation focused on difficult to distinguish cases
The paper proposes a different approach to data modeling. Analogous to the rejection method, where the misclassifications are removed and manually evaluated, we focus here on difficult to distinguish cases for binary classification. Such cases are further explored and information granulation is conducted based on the idea to stretch out the interesting intervals. This refinement model adopts the concept from database theory where the n x n relations are resolved through additional internal attribute connections. We introduce an integration of target functions for such cases. The achieved experimental results from a test dataset are described and future work for user assistance in knowledge modeling is presented.
modelling information granulation classification machine learning decision boundary