Classifying Inconsistency in AHP Pairwise Comparison Matrices Using Machine Learning

• URL: http://arxiv.org/abs/2505.06293v1
• Date: Wed, 07 May 2025 14:17:39 GMT
• Title: Classifying Inconsistency in AHP Pairwise Comparison Matrices Using Machine Learning
• Abstract summary: This study introduces a novel alternative that leverages triadic preference reversals (PR) to provide a more robust and interpretable assessment of consistency.<n>The PR method achieves 97% accuracy, significantly surpassing the Consistency Ratio (CR) method's 50%, with a false negative rate of only 2.6% compared to 5.5%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Assessing consistency in Pairwise Comparison Matrices (PCMs) within the Analytical Hierarchy Process (AHP) poses significant challenges when using the traditional Consistency Ratio (CR) method. This study introduces a novel alternative that leverages triadic preference reversals (PR) to provide a more robust and interpretable assessment of consistency. Triadic preference reversals capture inconsistencies between a pair of elements by comparing the direction of preference derived from the global eigenvector with that from a 3x3 submatrix (triad) containing the same pair, highlighting local-global preference conflicts. This method detects a reversal when one eigen ratio exceeds one while another falls below one, signaling inconsistency. We identify two key features: the proportion of preference reversals and the maximum reversal, which mediate the impact of a PCM's order on its consistency. Using these features simulated PCMs are clustered into consistent and inconsistent classes through k-means clustering, followed by training a logistic classifier for consistency evaluation. The PR method achieves 97\% accuracy, significantly surpassing the Consistency Ratio (CR) method's 50%, with a false negative rate of only 2.6\% compared to 5.5\%. These findings demonstrate the PR method's superior accuracy in assessing AHP consistency, thereby enabling more reliable decision-making. The proposed triadic preference reversal (PR) approach is implemented in the R package AHPtools publicly available on the Comprehensive R Archive Network (CRAN).

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