The Pearson Correlation Coefficient Applied to Compare Multi-Criteria Methods: Case the Ranking Problematic

Abounaima, Mohammed Chaouki; Mazouri, Fatima Zahra El; Lamrini, Loubna; Nfissi, Najib; Makhfi, Noureddine El; Ouzarf, Mohamed

doi:10.1109/iraset48871.2020.9092242

Cited by 8 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This present article is an extension of the paper published at the international conference IRASET'20 [1]. In this article, we have shown the importance of the correlation metric to evaluate the quality of the ranking results of the MCDA methods.…”

Section: Introductionmentioning

confidence: 75%

“…The ranking which gives a better quality will, therefore, be retained. In the first version of the proposed metric [1] no reference was made to the importance of the criteria. It was supposed that all the criteria are treated with the same importance, i.e.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

“…This amounts to measuring the dependence between the matrix R and each matrix R k . The dependence between the matrices is measured mathematically by the correlation coefficient [1]. To take into account all the correlations calculated as well as the relative importance of the criteria W, the quality measure Q(P) is then calculated by a weighted average, which is given by the following formula (11).…”

Section: Process Of the Extension Approachmentioning

confidence: 99%

See 2 more Smart Citations

Comparison by Correlation Metric the TOPSIS and ELECTRE II Multi-Criteria Decision Aid Methods: Application to the Environmental Preservation in the European Union Countries

Abounaima¹,

Lamrini²,

Makhfi³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

This article is part of the field of Multi-Criteria Decision Aid (MCDA), where several criteria must be considered in decision making. All criteria are generally as varied as possible and express different dimensions, and aspects of the decision problem posed. For more than four decades, several MCDA methods have emerged and have been applied perfectly to solve a large number of multi-criteria decision problems. Several studies have tried to compare these methods directly with one another. Since each method has its disadvantages and advantages, a direct comparison between the two methods is normally far from common sense and becomes subjective. In this article, we propose a rational and objective approach that will be used to compare the methods between them. This approach consists of using the famous correlation measure to evaluate the quality of the results obtained by different MCDA approaches. To prove the effectiveness of the proposed approach, experimental examples, as well as a study of real cases, will be studied. Indeed, a set of indicators, known as The Europe 2020 indicators, are defined by the European Commission (EC) to control the smart, sustainable and inclusive growth performance of the European Union countries (EU). In this proposed real study, a subset of indicators is used to compare the performance of environmental preservation and protection of the EU states. For this, the two-renowned methods MCDA ELECTRE II and TOPSIS are used to classify from the best to the worst CE countries with regard to environmental preservation. The results of the experiment that the proposed ranking quality measure is significant. For the case study shows that the ELECTRE II method results in a better ranking than that obtained by the TOPSIS method.

show abstract

Section: Introductionmentioning

confidence: 75%

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Section: Process Of the Extension Approachmentioning

confidence: 99%

See 1 more Smart Citation

Comparison by Correlation Metric the TOPSIS and ELECTRE II Multi-Criteria Decision Aid Methods: Application to the Environmental Preservation in the European Union Countries

Abounaima¹,

Lamrini²,

Makhfi³

et al. 2020

Adv. sci. technol. eng. syst. j.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The correlation coefficient is a measure of the link between two variables [1]. This coefficient is used to characterize a positive or negative relationship, and it is the symmetrical measure, the closer it is to 1, in absolute value, the link and the dependence between the two variables will be better.…”

Section: The Pearson Correlation Coefficientmentioning

confidence: 99%

“…In our recent work [1], we have demonstrated the usefulness of the Pearson Correlation metric to evaluate the quality of the ranking results obtained by different multi-criteria aggregation procedures (MCAP). We intend in this article to propose other possible metrics which take into account the relative importance of the criteria, which generalizes the case of an equal-weighting where all the criteria have the same importance.…”

Section: Introductionmentioning

confidence: 99%

New Metrics to Measure the Quality of the Ranking Results Obtained by the Multi-criteria Decision-Making Methods

Abounaima

Lamrini

Mazouri

et al. 2021

Lecture Notes in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Nowadays, there is a panoply of multi-criteria decision-making methods which are proposed in the literature to solve the ranking problematic, where each method has its resolution process and has its drawbacks and advantages. These methods aim to rank from best to worst a finite set of alternatives while taking into account a set of conflictual criteria. The purpose of this article is to propose specific metrics that will be useful to measure the quality of the rankings obtained by different methods. Thus, these quality measures can help the decision-maker to choose the best ranking objectively when adopting several methods. To show and prove the importance and relevance of the metrics proposed, a set of twenty-five examples of rankings will be examined. The results of the experiment conclusively show that all the proposed metrics lead to significant and equivalent quality measures.

show abstract

A Comparative Study and Tool to Early Predict Diabetes Using Various Machine and Deep Learning Based Techniques

Porwal

Patel

Tilwani

et al. 2021

Studies in Autonomic, Data-Driven and Industrial Computing

View full text Add to dashboard Cite

The Pearson Correlation Coefficient Applied to Compare Multi-Criteria Methods: Case the Ranking Problematic

Cited by 8 publications

References 15 publications

Comparison by Correlation Metric the TOPSIS and ELECTRE II Multi-Criteria Decision Aid Methods: Application to the Environmental Preservation in the European Union Countries

Comparison by Correlation Metric the TOPSIS and ELECTRE II Multi-Criteria Decision Aid Methods: Application to the Environmental Preservation in the European Union Countries

New Metrics to Measure the Quality of the Ranking Results Obtained by the Multi-criteria Decision-Making Methods

A Comparative Study and Tool to Early Predict Diabetes Using Various Machine and Deep Learning Based Techniques

Contact Info

Product

Resources

About