Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making

Roberto Todeschini, Francesca Grisoni, Davide Ballabio

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Ranking and multi-criteria decision-making approaches are useful tools to analyse multivariate data and obtain useful insights into data structure and the relationships between samples and variables. In this study, we present a new ranking approach, named Deep Ranking Analysis by Power Eigenvectors (DRAPE), which is based on the Power-Weakness Ratio analysis and provides a set of sequential rankings. Such a sequential ranking procedure allows to gather deeper insights into the analysed dataset. Moreover, by a “retro”-regression procedure, the relevance of each variable in determining the final rankings can be assessed, while a consensus ranking can be obtained by a Principal Component Analysis (PCA). In this study, we present the theory of the novel method, and show three applications to real datasets.
Original languageEnglish
Pages (from-to)129-137
Number of pages9
JournalChemometrics and Intelligent Laboratory Systems
Volume191
DOIs
Publication statusPublished - 15 Aug 2019
Externally publishedYes

Keywords

  • Tournament tables
  • Dominance matrices
  • Multi-criteria decision making
  • Ranking
  • Power-weakness ratio
  • PWR
  • MCDM

Fingerprint

Dive into the research topics of 'Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making'. Together they form a unique fingerprint.

Cite this