Pseudo datasets estimate feature attribution in artificial neural networks.

ABSTRACT: Neural networks demonstrate exceptional predictive performance across diverse classification tasks. However, their lack of interpretability restricts their widespread application. Consequently, in recent years, numerous researchers have focused on model explanation techniques to elucidate the internal mechanisms of these 'black box' models. Yet, prevailing explanation methods predominantly focus on elucidating individual features, thereby overlooking synergistic effects and interactions among multiple features, potentially hindering a comprehensive understanding of the model's predictive behavior. Therefore, this study proposes a two-stage explanation method, known as Pseudo Datasets Perturbation Effect (PDPE). The fundamental concept is to discern feature importance by perturbing the data

SUBMITTER: Yang HY 

PROVIDER: S-EPMC12749704 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature