Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation

López, Juan Carlos; Noriega, Jesús Jaime Solano; Figueira, José Rui; Li, Jun; Chavira, Diego Alonso Gastélum

doi:10.1016/j.ejor.2020.12.026

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Therefore, we need specific procedures in order to derive a consensus ranking. We propose the procedure which finds its roots in [31,58,59]. Our approach for exploitation a fuzzy outranking relation to derive a ranking is to use a multiobjective evolutionary algorithm-based heuristic method.…”

Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

“…Because of space limitations, we omitted the presentation of the ranking procedure. In order to address this gap, the reader can consult the paper [31].…”

Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

“…A list of significant disease-related genes derived from the integrative framework using RNA-Seq, protein interaction, tissue and TF cotargeting network data are ranked by our outranking method in decreasing order of preference. The method is based on ELECTRE III [30] to create a fuzzy outranking relation and then, it uses a multiobjective evolutionary algorithm [31] to exploit the outranking relation and to derive a prioritized list of candidate disease genes. The approach takes into consideration criteria including network centralities.…”

Section: Introductionmentioning

confidence: 99%

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An Outranking Approach for Gene Prioritization Using Multinetworks

Noriega¹,

López²,

Browne³

et al. 2021

IJCIS

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High-throughput experimental techniques such as genome-wide association studies have been instrumental in the identification of disease-associated genes. These methods often produce large lists of disease candidate genes which are time-consuming and expensive to experimentally validate. Computational gene prioritization methods are required to identify relevant genes from a larger pool of candidates. Research has shown that the integration of diverse "omic" evidence can reduce the candidategene search space. In this paper we present a general framework that integrates "omic" data using a multinetwork approach and topological analysis to prioritize disease-candidate genes. Specifically, we propose a data integration method within a multicriteria decision analysis context using aggregation mechanisms based on decision rules identifying positive and negative criteria for judging gene-candidates ranks. The proposed multinetwork disease gene prioritization method is applied to the prioritization of disease genes in ovarian cancer progression. Using this approach we uncovered known ovarian cancer genes GSTA1, ERBB2, IL1A, MAGEB2, along with significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ErbB signaling and pathways in cancer. Relatively high predictive performance (area under Receiver Operating Characteristic [ROC] curve 0.704) was observed when classifying epithelial ovarian high-grade serous carcinoma cancer early and late stage RNA-Seq expression profiles from individuals using 10-fold cross-validation.

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Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

“…Because of space limitations, we omitted the presentation of the ranking procedure. In order to address this gap, the reader can consult the paper [31].…”

Section: Outranking Approach For Multi-network Disease Gene Prioritizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Outranking Approach for Gene Prioritization Using Multinetworks

Noriega¹,

López²,

Browne³

et al. 2021

IJCIS

Self Cite

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“…The multicriteria decision analysis tool used to carry out the evaluation and final ranking of the capital cities is the hierarchical ELECTRE III (h-ELECTRE III) method [23], [24], which through its criteria hierarchy, outranking, and preference and indifference thresholds concepts make it an acceptable way to be used in situations such as those presented in the study. It should be noted that for the exploitation of the comprehensive fuzzy outranking relation and the partial fuzzy outranking relations related to non-elementary criteria, a multiobjective evolutionary algorithm (MOEA) was used [20], [25]. The result of this MOEA is a ranking (a total preorder of classes of alternatives) of the 31 capital cities with the best public security in decreasing order of evaluation.…”

Section: Introductionmentioning

confidence: 99%

Multicriteria Decision Model to Support the Evaluation of Common Jurisdiction Violence in the Capital Cities of the States of Mexico

et al. 2023

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The capital cities of the states of the Mexican Republic present a considerable heterogeneity in terms of their level of public security, so their correct characterization is an essential requirement, but not sufficient, for the elaboration of public policies for the prevention of crimes in the common jurisdiction. In this paper, we propose a multicriteria decision model based on the hierarchical ELECTRE III method that assists in how a policymaker can carry out this task. The objective of the work is to measure and compare the incidence of crimes of common jurisdiction in the capital cities of the states of the Mexican Republic. This model compares public security in 31 capital cities, adapting the situation as a multicriteria ranking problem to order the different capitals by their level of public security. The results showed that the model could identify the level of comprehensive public security of a capital city compared to the rest. Similarly, it also shows the disparities of this phenomenon between capital cities in relation to high, medium and low impact crimes.

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“…NSGA-II is based on reducing the differences between the valued outranking relation 𝑆𝑆 𝐴𝐴 𝜎𝜎 and the final ranking. More information on this algorithm is provided in the work byLeyva-López et al (2021).…”

mentioning

confidence: 99%

A Personnel Selection Model for a Software Development Company based on the ELECTRE III Method and a Variant of NSGA-II

Leyva-Lopez

Noriega

Chavira

et al. 2022

Innovar

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Personnel selection represents a valuable decision-making process that determines, in some way, the competitiveness and performance of an organization. The essential elements of the personnel selection task are the position requirements and the accessible information related to candidates. In this paper, the personnel selection task is modeled as a multicriteria decision-making problem, considering varied competencies and skills to assess the applicants for a specific position. We used this multicriteria model to solve personnel selection cases using sadgage, a decision support system that solves examples of the multicriteria ranking problem in decreasing order based on the preferences of the decision-maker. The electre-iii method is embedded in the software in order to construct a fuzzy outranking relation and a multi-objective evolutionary algorithm to exploit such relation and generate a ranking as a recommendation. This work presents a practical application to a personnel selection problem that evaluates a group of applicants to a software developer job in an enterprise based in northwestern Mexico. We show how the proposed multicriteria procedure offers a recommendation to the decision-maker about the applicants in decreasing order of preferences.

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Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation

Cited by 10 publications

References 29 publications

An Outranking Approach for Gene Prioritization Using Multinetworks

An Outranking Approach for Gene Prioritization Using Multinetworks

Multicriteria Decision Model to Support the Evaluation of Common Jurisdiction Violence in the Capital Cities of the States of Mexico

A Personnel Selection Model for a Software Development Company based on the ELECTRE III Method and a Variant of NSGA-II

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