Muticriteria decision making based on independent component analysis: A preliminary investigation considering the TOPSIS approach

TL;DR

This paper introduces a novel multi-criteria decision-making method that combines independent component analysis with TOPSIS to improve ranking accuracy when criteria are correlated.

Contribution

It presents a new approach integrating ICA with TOPSIS for decision making, addressing correlations among criteria that traditional methods overlook.

Findings

ICA-based approach outperforms existing methods in numerical experiments

Latent variables provide better decision rankings

Method effectively handles correlated criteria

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