Enhancing Cost-Sensitive Tree-Based XAI Surrogate Method: Exploring Alternative Cost Matrix Formulation

This study investigates how different cost matrix formulations influence cost-sensitive tree extraction method performance within the post-hoc model-agnostic XAI framework. As an input parameter, the cost matrix is essential in building cost-sensitive tree models. The initial, default version of the cost matrix is defined to reflect the class imbalance ratio among each pair of classes. Here, two different formulations of the alternative cost matrix are proposed: centroid distance-based and medoid distance-based cost matrix. The cost-sensitive tree method with different formulations of cost-matrix is compared against other tree-based and rule-based XAI methods as a surrogate model for the underlying black-box model. Evaluation metrics are employed to assess the generated explanations, and results demonstrate that rule sets extracted from cost-sensitive trees are smaller with shorter rules on average across different datasets with varying number of classes.