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

Author

Listed:
  • R Setiono

    (National University of Singapore)

  • S-L Pan

    (National University of Singapore)

  • M-H Hsieh

    (Yuan Ze University)

  • A Azcarraga

    (De LaSalle University)

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.

Suggested Citation

  • R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2005. "Automatic knowledge extraction from survey data: learning M-of-N constructs using a hybrid approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(1), pages 3-14, January.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:1:d:10.1057_palgrave.jors.2601807
    DOI: 10.1057/palgrave.jors.2601807
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    References listed on IDEAS

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    1. Melody Y. Kiang & Ajith Kumar, 2001. "An Evaluation of Self-Organizing Map Networks as a Robust Alternative to Factor Analysis in Data Mining Applications," Information Systems Research, INFORMS, vol. 12(2), pages 177-194, June.
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    Cited by:

    1. Y Hayashi & M-H Hsieh & R Setiono, 2009. "Predicting consumer preference for fast-food franchises: a data mining approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1221-1229, September.
    2. R Setiono & S-L Pan & M-H Hsieh & A Azcarraga, 2006. "Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(3), pages 231-240, March.

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