Enhancing Counterfactual Explanations with Feasibility and Diversity

Counterfactual explanations (CFEs) are essential for interpreting machine learning model predictions by illustrating how minimal input changes can alter outcomes in counterfactual scenarios. Effective CFEs should satisfy two key properties: feasibility and diversity. Feasibility ensures that the generated CFEs are realistic and applicable across various contexts, while diversity provides multiple perspectives on model predictions. However, existing methods face significant limitations, such as difficulty in generating multiple CFEs and reliance on a convex loss landscape, which hinders adaptability to complex scenarios and diverse applications. To overcome these challenges, we propose a novel approach for generating diverse CFEs applicable to both convex and non-convex machine learning models. Our method leverages a Farthest Point Sampling-enhanced Genetic Algorithm to improve diversity, while a sample-based Manhattan distance regularization enhances feasibility. Experimental results demonstrate that our approach outperforms state-of-the-art methods in generating CFEs that are both feasible and diverse.