Muticriteria Decision Making Based on Independent Component Analysis: A Preliminary Investigation Considering the TOPSIS Approach

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Author(s):	Pelegrina, Guilherme Dean ; Duarte, Leonardo Tomazeli ; Travassos Romano, Joao Marcos ; Deville, Y ; Gannot, S ; Mason, R ; Plumbley, MD ; Ward, D Total Authors: 8
Document type:	Journal article
Source:	LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2018); v. 10891, p. 10-pg., 2018-01-01.
Abstract
This work proposes the application of independent component analysis to the problem of ranking different alternatives by considering criteria that are not necessarily statistically independent. In this case, the observed data (the criteria values for all alternatives) can be modeled as mixtures of latent variables. Therefore, in the proposed approach, we perform ranking by means of the TOPSIS approach and based on the independent components extracted from the collected decision data. Numerical experiments attest the usefulness of the proposed approach, as they show that working with latent variables leads to better results compared to already existing methods. (AU)

FAPESP's process:	16/21571-4 - Multigroup and multiple criteria decision analysis methods: models based on information processing
Grantee:	Guilherme Dean Pelegrina
Support Opportunities:	Scholarships in Brazil - Doctorate


FAPESP's process:	17/23879-9 - Choquet integrals in multi-group multi-criteria decision making
Grantee:	Guilherme Dean Pelegrina
Support Opportunities:	Scholarships abroad - Research Internship - Doctorate