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

Roberto Todeschini; Francesca Grisoni; Davide Ballabio

doi:10.1016/j.chemolab.2019.06.005

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 journal › Article › Academic › peer-review

6 Citations (SciVal)

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 language	English
Pages (from-to)	129-137
Number of pages	9
Journal	Chemometrics and Intelligent Laboratory Systems
Volume	191
DOIs	https://doi.org/10.1016/j.chemolab.2019.06.005
Publication status	Published - 15 Aug 2019
Externally published	Yes

Keywords

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

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10.1016/j.chemolab.2019.06.005

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

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.",

keywords = "Tournament tables, Dominance matrices, Multi-criteria decision making, Ranking, Power-weakness ratio, PWR, MCDM",

author = "Roberto Todeschini and Francesca Grisoni and Davide Ballabio",

year = "2019",

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doi = "10.1016/j.chemolab.2019.06.005",

language = "English",

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AU - Todeschini, Roberto

AU - Grisoni, Francesca

AU - Ballabio, Davide

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Y1 - 2019/8/15

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AB - 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.

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KW - Dominance matrices

KW - Multi-criteria decision making

KW - Ranking

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KW - PWR

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JO - Chemometrics and Intelligent Laboratory Systems

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

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Keywords

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