We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a computationally feasible search method that is particularly well suited for random forests, denoted by changeforest. However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a k-nearest neighbor classifier. We prove that it consistently locates change points in single change point settings when paired with a consistent classifier. Our proposed method changeforest achieves improved empirical performance in an extensive simulation study compared to existing multivariate nonparametric change point detection methods. An efficient implementation of our method is made available for R, Python, and Rust users in the changeforest software package.

翻译：我们提出了一种新颖的多变量非参数多重变点检测方法，该方法利用分类器技术。通过构建基于分类器对数似然比的统计量，利用类别概率预测来比较不同的变点配置。我们提出了一种计算可行的搜索方法，特别适用于随机森林（称为changeforest），但该方法也可与任何能输出类别概率预测的分类器配合使用——我们通过k近邻分类器进行了验证。理论证明，当与一致分类器配对时，该方法在单一变点场景下能一致地定位变点。与现有非参数多变量变点检测方法相比，我们提出的changeforest方法在广泛仿真研究中展现出更优的经验性能。该方法在changeforest软件包中为R、Python和Rust用户提供了高效实现。