Automatic knowledge extraction from survey data: learning M-of-N constructs using a hybrid approach

• Published in: The Journal of the Operational Research Society 2005-01, Vol.56 (1), p.3-14
• Main Authors: Setiono, R, Pan, S-L, Hsieh, M-H, Azcarraga, A
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial intelligence; Artificial neural networks; Automobiles; Back propagation; Bond ratings; Brands; Case-Oriented Papers; Classification; Computer science; control theory; systems; Connectionism. Neural networks; Datasets; Decision trees; Exact sciences and technology; Learning; M-of-N constructs; Machine learning; Neural networks; Operations research; Pruning; Studies; Survey data; Test data
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/palgrave.jors.2601807

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.