  Identifying the relevant input features that have a critical influence on the
output results is indispensable for the development of explainable artificial
intelligence (XAI). Remove-and-Retrain (ROAR) is a widely accepted approach for
assessing the importance of individual pixels by measuring changes in accuracy
following their removal and subsequent retraining of the modified dataset.
However, we uncover notable limitations in pixel-perturbation strategies. When
viewed from a geometric perspective, we discover that these metrics fail to
discriminate between differences among feature attribution methods, thereby
compromising the reliability of the evaluation. To address this challenge, we
introduce an alternative feature-perturbation approach named Geometric
Remove-and-Retrain (GOAR). Through a series of experiments with both synthetic
and real datasets, we substantiate that GOAR transcends the limitations of
pixel-centric metrics.
