发现距离分类器的隐藏神经结构，让解释方法更准确。
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
- 将距离分类器重构为带非线性池化的线性探测单元结构。
- 在多个数据集上比基线方法解释更准确，提升显著。
- 适合需要可解释性的科研与工业场景，如医疗、金融建模。
基于距离的分类器（如k近邻和支撑向量机）在科学与工业中仍是主流机器学习工具。为使这些模型预测更具可解释性，本文揭示了其内部隐藏的神经网络结构：由线性检测单元与非线性池化层构成。这一结构使解释性技术（如逐层重要性传播，LRP）得以应用。通过定量评估，新方法在多个基准测试中优于现有方案。此外，我们通过两个实际案例验证了该解释框架的有效性，证明其在真实场景中的实用价值。
原文摘要 · Abstract (English)
Distance-based classifiers, such as k-nearest neighbors and support vector machines, continue to be a workhorse of machine learning, widely used in science and industry. In practice, to derive insights from these models, it is also important to ensure that their predictions are explainable. While the field of Explainable AI has supplied methods that are in principle applicable to any model, it has also emphasized the usefulness of latent structures (e.g. the sequence of layers in a neural network) to produce explanations. In this paper, we contribute by uncovering a hidden neural network structure in distance-based classifiers (consisting of linear detection units combined with nonlinear pooling layers) upon which Explainable AI techniques such as layer-wise relevance propagation (LRP) become applicable. Through quantitative evaluations, we demonstrate the advantage of our novel explanation approach over several baselines. We also show the overall usefulness of explaining distance-based models through two practical use cases.
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