Please use this identifier to cite or link to this item:
Title: Automatic knowledge extraction from survey data: Learning M-of-N constructs using a hybrid approach
Authors: Setiono, R. 
Pan, S.-L. 
Hsieh, M.-H.
Azcarraga, A.
Keywords: Decision trees
M-of-N constructs
Neural networks
Issue Date: 2005
Citation: Setiono, R., Pan, S.-L., Hsieh, M.-H., Azcarraga, A. (2005). Automatic knowledge extraction from survey data: Learning M-of-N constructs using a hybrid approach. Journal of the Operational Research Society 56 (1) : 3-14. ScholarBank@NUS Repository.
Abstract: 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 M-of-N constructs. An M-of-N construct is a rule condition that is satisfied if (at least, exactly, at most) M of the N binary attributes in the construct are present. The effectiveness of the method is illustrated on data collected for a study of global car market segmentation. The results show that besides achieving high predictive accuracy, the method also allows meaningful interpretation of the relationships among the data variables.
Source Title: Journal of the Operational Research Society
ISSN: 01605682
DOI: 10.1057/palgrave.jors.2601807
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Jan 12, 2022


checked on Jan 12, 2022

Page view(s)

checked on Jan 13, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.