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 nxn 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 modelling is presented. Key words: Information Granulation, Modelling, Decision Boundary, Classification, Machine Learning.

• Type: Conference
• Year: 2007
• Where: COMPSYSTECH
• Authors: Plamena Andreeva, Plamen Andreev, Maya Dimitrova, Petia Radeva
• Keywords: Additional Internal Attribute, COMPSYSTECH 2007, Hardware, Information Granulation, Such Cases