新算法FAMeX通过特征关联图提升AI决策可解释性
A New Technique for AI Explainability using Feature Association Map
- 基于特征关联图构建可解释性模型
- 在8个基准算法上优于PFI和SHAP
- 适合需要可信AI解释的场景
AI系统缺乏透明性，在关键应用中带来挑战。确保AI决策可解释是建立信任的关键。本文提出一种新型可解释AI算法FAMeX（基于特征关联图的可解释性），其核心是将特征集建模为特征关联图（FAM），基于特征间关联关系进行解释。实验在8个基准算法上验证，FAMeX在分类任务中对特征重要性的评估优于现有方法如排列特征重要性（PFI）和SHapley加性解释（SHAP）。结果表明FAMeX是解释AI预测的有力候选。
原文摘要 · Abstract (English)
Lack of transparency in AI systems poses challenges in critical real-life applications. It is important to be able to explain the decisions of an AI system to ensure trust on the system. Explainable AI (XAI) algorithms play a vital role in achieving this objective. In this paper, we are proposing a new algorithm for Explaining AI systems, FAMeX (Feature Association Map based eXplainability). The proposed algorithm is based on a graph-theoretic formulation of the feature set termed as Feature Association Map (FAM). The foundation of the modelling is based on association between features. The proposed FAMeX algorithm has been found to be better than the competing XAI algorithms - Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP). Experiments conducted with eight benchmark algorithms show that FAMeX is able to gauge feature importance in the context of classification better than the competing algorithms. This definitely shows that FAMeX is a promising algorithm in explaining the predictions from an AI system
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