Hierarchical Data Envelopment Analysis for Classification of High-Dimensional Data

Ming-Miin Yu (), Kok Fong See and Bo Hsiao
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Ming-Miin Yu: National Taiwan Ocean University
Bo Hsiao: Chang Jung Christian University

A chapter in Data-Enabled Analytics, 2021, pp 199-229 from Springer

Abstract: 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.

Keywords: Hierarchical DEA; High-dimensional data; Performance; Food security (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-75162-3_8

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