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1 Annals of Computer Science and Information Systems

1 2023

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An Innovative Drastic Metric for Ranking Similarity in Decision-Making Problems

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Sałabun W. , Shekhovtsov A.

2023

tom Vol. 35

731--738

In this paper, we propose a novel approach to distance measurement for rankings, introducing a new metric that exhibits exceptional properties. Our proposed distance metric is defined within the interval of 0 to 1, ensuring a compact and standardized representation. Importantly, we demonstrate that this distance metric satisfies all the essential criteria to be classified as a true metric. By adhering to properties such as non-negativity, identity of indiscernibles, symmetry, and the crucial triangle inequality, our proposed distance metric provides a robust and reliable approach for comparing rankings in a rigorous and mathematically sound manner. Finally, we compare our new metric with distances such as Hamming distance, Canberra distance, Bray-Curtis distance, Euclidean distance, Manhattan distance, and Chebyshev distance. By conducting simple experiments, we assess the performance and advantages of our proposed metric in comparison to these established distance measures. Through these comparisons, we demonstrate the superior properties and capabilities of our new drastic weighted similarity distance for accurately capturing the dissimilarities and similarities between rankings in the decision-making domain.