Gaussian Process Regression With Interpretable Sample-Wise Feature Weights
Gaussian Process Regression With Interpretable Sample-Wise Feature Weights
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DOI：
10.1109/tnnls.2021.3131234
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发表时间：
2020-07
影响因子：
10.4
通讯作者：
Yuya Yoshikawa;Tomoharu Iwata
中科院分区：
文献类型：
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作者：
Yuya Yoshikawa;Tomoharu Iwata
高斯过程回归（GPR）是机器学习中的一个基本模型。由于其预测精度高、不确定性强、核处理各种数据结构的通用性，GPR已成功地应用于各种领域。然而，在探地雷达中，无法解释输入的特征如何有助于其预测。在此，我们提出了具有局部解释的探地雷达，在保持探地雷达预测性能的同时，揭示了每个样本的特征对预测的贡献。在提出的模型中，每个样本的预测和解释都是使用易于解释的局部线性模型进行的。假设局部线性模型的权向量由多元高斯过程先验产生。通过最大化边际似然来估计模型的超参数。对于一个新的测试样本，该模型能够以封闭的形式预测其目标变量和权向量的值及其不确定性。在各种基准数据集上的实验结果表明，该模型的预测性能与GPR相当，优于现有的可解释模型，在定量和定性上都具有更高的可解释性。
Gaussian process regression (GPR) is a fundamental model used in machine learning (ML). Due to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications. However, in GPR, how the features of an input contribute to its prediction cannot be interpreted. Here, we propose GPR with local explanation, which reveals the feature contributions to the prediction of each sample while maintaining the predictive performance of GPR. In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model. The weight vector of the locally linear model is assumed to be generated from multivariate Gaussian process priors. The hyperparameters of the proposed models are estimated by maximizing the marginal likelihood. For a new test sample, the proposed model can predict the values of its target variable and weight vector, as well as their uncertainties, in a closed form. Experimental results on various benchmark datasets verify that the proposed model can achieve predictive performance comparable to those of GPR and superior to that of existing interpretable models and can achieve higher interpretability than them, both quantitatively and qualitatively.