One-class Classification (OCC) is an important field of machine learning which aims at predicting a single class on the basis of its lonely representatives and potentially some additional counter-examples. OCC is thus opposed to traditional classification problems involving two or more classes, and addresses the issue of class unbalance. There is a wide range of one-class models which give satisfaction in terms of performance. But at the time of explainable artificial intelligence, there is an increasing need for interpretable models. The present work advocates a novel one-class model which tackles this challenge. Within a greedy and recursive approach, our proposal for an explainable One-Class decision Tree (OC-Tree) rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Thus, the OC-Tree encloses data within hyper-rectangles of interest which can be described by a set of rules. Against state-of-the-art methods such as Cluster Support Vector Data Description (ClusterSVDD), One-Class Support Vector Machine (OCSVM) and isolation Forest (iForest), the OC-Tree performs favorably on a range of benchmark datasets. Furthermore, we propose a real medical application for which the OC-Tree has demonstrated effectiveness, through the ability to tackle interpretable medical diagnosis aid based on unbalanced datasets.

一类分类（OCC）是机器学习的重要领域，旨在基于其孤独的代表和可能的其他反例来预测单个类。因此，OCC反对涉及两个或多个类别的传统分类问题，并解决了类别不平衡的问题。有各种各样的一类模型，它们使性能令人满意。但是在可解释的人工智能时代，对可解释模型的需求日益增长。本工作提倡一种新颖的一类模型来应对这一挑战。在一种贪婪和递归的方法中，我们提出的可解释的一类决策树（OC-Tree）提案基于内核密度估计，可以基于一个或几个感兴趣的间隔来分割数据子集。从而，OC-Tree将数据封装在感兴趣的超矩形内，可以用一组规则来描述。与集群支持向量数据描述（ClusterSVDD），一类支持向量机（OCSVM）和隔离林（iForest）等最新方法相比，OC-Tree在一系列基准数据集上的表现均令人满意。此外，我们提出了一种真正的医疗应用，OC-Tree通过基于不平衡数据集处理可解释的医疗诊断工具的能力，证明了其有效性。OC-Tree在一系列基准数据集上表现出色。此外，我们提出了一种真正的医疗应用，OC-Tree通过基于不平衡数据集处理可解释的医疗诊断工具的能力，证明了其有效性。OC-Tree在一系列基准数据集上表现出色。此外，我们提出了一种真正的医疗应用，OC-Tree通过基于不平衡数据集处理可解释的医疗诊断工具的能力，证明了其有效性。