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Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data

In: Data-Enabled Analytics

Author

Listed:
  • Ming-Miin Yu

    (National Taiwan Ocean University)

  • Kok Fong See

    (Universiti Sains Malaysia
    University of Massachusetts)

  • Bo Hsiao

    (Chang Jung Christian University)

Abstract

Hierarchical data envelopment analysis (H-DEA) is a model extension of conventional data envelopment analysis in assigning weights using a number of attributes and sub attributes in a hierarchical setting. The objective of this chapter is to examine global food security performance using H-DEA model and later uses multi-level K means clustering approach to cluster sampled countries into homogeneous and distinct groups. Under proposed H-DEA with clustering approach, the results will help policy makers to understand the benchmarking process and identify efficiency contributions of the global food security attributes. Furthermore, the findings can be used to assist countries in projecting learning path from other high-performing nations. Such path information doesn’t exist when country grouping is carried out using personal judgement thus reduces subjectivity in measuring multiple food security performance attributes.

Suggested Citation

  • Ming-Miin Yu & Kok Fong See & Bo Hsiao, 2021. "Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 199-229, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-75162-3_8
    DOI: 10.1007/978-3-030-75162-3_8
    as

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