Article ID: | iaor20062355 |
Country: | United Kingdom |
Volume: | 56 |
Issue: | 1 |
Start Page Number: | 3 |
End Page Number: | 14 |
Publication Date: | Jan 2005 |
Journal: | Journal of the Operational Research Society |
Authors: | Setiono R., Pan S.L., Hsieh M.-H., Azcarraga A. |
Keywords: | computers: data-structure |
Data collected from a survey typically consist of attributes that are mostly if not completely binary-valued or binary-encoded. We present a method for handling such data where the underlying data analysis can be cast as a classification problem. We propose a hybrid method that combines neural network and decision tree methods. The network is trained to remove irrelevant data attributes and the decision tree is applied to extract comprehensible classification rules from the trained network. The conditions of the rules are in the form of a conjunction of