Classifying Inconsistency in AHP Pairwise Comparison Matrices Using Machine Learning (2505.06293v1)

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).