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Handbook of Multicriteria Analysis pp 215–240Cite as

Disaggregation Analysis and Statistical Learning: An Integrated Framework for Multicriteria Decision Support

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Part of the book series: Applied Optimization ((APOP,volume 103))

Abstract

Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this chapter the relationships between the two fields are explored. The differences and similarities between the two approaches are identified and a review is given regarding the integration of the two fields.

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Authors and Affiliations

  1. Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece

    Michael Doumpos & Constantin Zopounidis

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  1. Michael Doumpos
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  2. Constantin Zopounidis
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Correspondence to Michael Doumpos .

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Editors and Affiliations

  1. Dept. Production Engineering, and Management, Technical University of Crete, University Campus, Chania, 731 00, Greece

    Constantin Zopounidis

  2. Dept. Industrial & Systems, Engineering, University of Florida, Weil Hall 303, Gainesville, 32611-6595, USA

    Panos M. Pardalos

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Doumpos, M., Zopounidis, C. (2010). Disaggregation Analysis and Statistical Learning: An Integrated Framework for Multicriteria Decision Support. In: Zopounidis, C., Pardalos, P. (eds) Handbook of Multicriteria Analysis. Applied Optimization, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92828-7_7

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