Integrating multicriteria decision making and principal component analysis: a systematic literature review

Autor: Arthur Pinheiro de Araújo Costa, Ricardo Choren, Daniel Augusto de Moura Pereira, Adilson Vilarinho Terra, Igor Pinheiro de Araújo Costa, Claudio de Souza Rocha Junior, Marcos dos Santos, Carlos Francisco Simões Gomes, Miguel Ângelo Lellis Moreira
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Cogent Engineering, Vol 11, Iss 1 (2024)
Druh dokumentu: article
ISSN: 23311916
2331-1916
DOI: 10.1080/23311916.2024.2374944
Popis: Decision-support methods are crucial for analyzing complex alternatives and criteria in today’s data-driven world. This Systematic Literature Review (SLR) explores and synthesizes knowledge about decision support methodologies that integrate Multicriteria Decision Making (MCDM) and Principal Component Analysis (PCA), an unsupervised Machine Learning (ML) technique. Both techniques optimize complex decisions by combining multiple criteria and dimensional data analysis. Focusing on performance evaluations, criterion weighting, and validation testing, this review identifies significant gaps in existing methodologies. These include the lack of consideration for non-beneficial criteria in PCA, insufficient validation tests in over half of the studies, and the non-use of communalities (the contribution of each criterion to the main factors) in decision support approaches. Additionally, this SLR offers a comprehensive quantitative overview, analyzing data from the Scopus, IEEE, and Web of Science databases and identifying 16 relevant studies. Furthermore, the scarcity of systematic reviews integrating MCDM and PCA techniques impedes evidence-based decision-making practices and theoretical evolution. This is particularly crucial as ML and data analysis advance rapidly, requiring models that reflect technological innovations. This article addresses this gap in the literature by providing an analysis of decision support methods and guiding further improvement in this field.
Databáze: Directory of Open Access Journals
Externí odkaz: