Abstract
Explainable Artificial Intelligence (XAI) methods, such as Local Interpretable Model-Agnostic Explanations (LIME), have advanced the interpretability of black-box machine learning models by approximating their behavior locally using interpretable surrogate models. However, LIME's inherent randomness in perturbation and sampling can lead to locality and instability issues, especially in scenarios with limited training data. In such cases, data scarcity can result in the generation of unrealistic variations and samples that deviate from the true data manifold. Consequently, the surrogate model may fail to accurately approximate the complex decision boundary of the original model. To address these challenges, we propose a novel Instance-based Transfer Learning LIME framework (ITL-LIME) that enhances explanation fidelity and stability in data-constrained environments. ITL-LIME introduces instance transfer learning into the LIME framework by leveraging relevant real instances from a related source domain to aid the explanation process in the target domain. Specifically, we employ clustering to partition the source domain into clusters with representative prototypes. Instead of generating random perturbations, our method retrieves pertinent real source instances from the source cluster whose prototype is most similar to the target instance. These are then combined with the target instance's neighboring real instances. To define a compact locality, we further construct a contrastive learning-based encoder as a weighting mechanism to assign weights to the instances from the combined set based on their proximity to the target instance. Finally, these weighted source and target instances are used to train the surrogate model for explanation purposes.
Abstract (translated)
可解释的人工智能（XAI）方法，例如局部可解释模型无关解释（LIME），通过使用可解释的替代模型来近似黑盒机器学习模型的行为，从而提高了模型的透明度。然而，LIME在扰动和采样过程中固有的随机性可能会导致局部性和不稳定问题，尤其是在训练数据有限的情况下。在这种情况下，数据稀缺可能导致生成不现实的变化和样本，这些样本偏离了真实的数据流形。因此，替代模型可能无法准确地近似原始模型的复杂决策边界。 为了解决这些问题，我们提出了一种基于实例的迁移学习LIME框架（ITL-LIME），该框架在数据受限环境中提高了解释的精确性和稳定性。通过利用相关源域中的实际样本来帮助目标领域的解释过程，ITL-LIME将实例转移学习引入到LIME框架中。具体来说，我们采用聚类方法将源领域划分成具有代表原型的集群。我们的方法不是生成随机扰动，而是从与目标样本最相似的源簇中检索相关的实际源样本，并将其与目标样本附近的实际样本相结合。 为了定义一个紧凑的空间局部区域，我们进一步构建了一个基于对比学习的编码器作为加权机制，用于根据结合集合中的实例相对于目标样本的距离分配权重。最后，使用这些加权后的源和目标实例来训练替代模型以供解释用途。这种框架旨在提高在数据稀少情况下的模型可解释性的准确性和稳定性。
URL
https://arxiv.org/abs/2508.13672
