For supervised classification problems involving design, control, other practical purposes, users are not only interested in finding a highly accurate classifier, but they also demand that the obtained classifier be easily interpretable. While the definition of interpretability of a classifier can vary from case to case, here, by a humanly interpretable classifier we restrict it to be expressed in simplistic mathematical terms. As a novel approach, we represent a classifier as an assembly of simple mathematical rules using a non-linear decision tree (NLDT). Each conditional (non-terminal) node of the tree represents a non-linear mathematical rule (split-rule) involving features in order to partition the dataset in the given conditional node into two non-overlapping subsets. This partitioning is intended to minimize the impurity of the resulting child nodes. By restricting the structure of split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is assured. The non-linear split-rule at a given conditional node is obtained using an evolutionary bilevel optimization algorithm, in which while the upper-level focuses on arriving at an interpretable structure of the split-rule, the lower-level achieves the most appropriate weights (coefficients) of individual constituents of the rule to minimize the net impurity of two resulting child nodes. The performance of the proposed algorithm is demonstrated on a number of controlled test problems, existing benchmark problems, and industrial problems. Results on two to 500-feature problems are encouraging and open up further scopes of applying the proposed approach to more challenging and complex classification tasks.
LHT: Statistically-Driven Oblique Decision Trees for Interpretable Classification
Department of Automation（自动化系）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Department of Computing Science（计算机科学系）；University of Alberta（阿尔伯塔大学）
Interpretable clustering via optimal multi-way decision trees
Graduate School of Science and Technology, University of Tsukuba（科学技术研究生院，东京大学）；Institute of Systems and Information Engineering, University of Tsukuba（系统与信息工程研究所，东京大学）
Towards symbolic regression for interpretable clinical decision scores
Federal University of ABC（ABC联邦大学）；University of Pennsylvania（宾夕法尼亚大学）；Institute for Biomedical Informatics, University of Pennsylvania（宾夕法尼亚大学生物医学信息学研究所）；Boston Children's Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）
Department of Computer Science, Duke University, Durham, USA（杜克大学计算机科学系）；Department of Computer Science, University of British Columbia, Vancouver, Canada（不列颠哥伦比亚大学计算机科学系）
MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
Department of Mathematics and Computer Science, Gannan Normal University（赣南师范大学数学与计算机科学学院）；Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University（江西省教育研究院数据科学与人工智能重点实验室）；Department of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程学院）
School of Computer Science（计算机科学学院）；Electronic Engineering University of Essex Colchester, United Kingdom（埃塞克斯大学电子工程学院伦敦郡，英国）；Department of Mathematics Slovak Academy of Sciences Bratislava, Slovakia（数学系斯洛伐克科学院布拉迪斯拉发，斯洛伐克）
RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
Department of Computer Science and Technology, University of Cambridge（计算机科学与技术系，剑桥大学）；U.S. Army Medical Research and Development Command（美国陆军医疗研究与开发指挥部）
DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization
Data Science Center of Excellence, Faculty of Military Sciences, Breda, NL（数据科学卓越中心，军事科学学院，荷兰布雷达）；NLR - Royal Netherlands Aerospace Centre, Amsterdam, NL（NLR-荷兰航空航天研究中心，荷兰阿姆斯特丹）；Departement Intelligent Systems, Tilburg University, Tilburg, NL（智能系统部门，蒂尔堡大学，荷兰蒂尔堡）
Beyond Point Prediction: Artificial Representative Trees with Uncertainty
University of Luebeck（吕贝克大学）；University Medical Center Göttingen（哥廷根大学医学中心）；Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine（下萨克森州医学人工智能与因果方法中心）；Jönköping University（延雪平大学）
AXA AI Research（AXA人工智能研究）；Stanford University（斯坦福大学）；EPFL, Lausanne, Switzerland（瑞士洛桑联邦理工学院）；Polish Academy of Sciences, IBS PAN, Warsaw, Poland（波兰科学院计算机科学研究所）
A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length
Department of Mathematics and Computer Science, Gannan Normal University（数学与计算机科学学院，赣南师范大学）；Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University（江西省数据科学与人工智能重点实验室，赣南师范大学）；Department of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；Department of Electrical and Computer Engineering, University of Alberta（电气与计算机工程学院，阿尔伯塔大学）
Department of Electrical Engineering, Chalmers University of Technology（楚基默斯理工大学电子工程系）；Department of Computer Science, University of Waikato（瓦卡托大学计算机科学系）
Unity Forests: Improving Interaction Modelling and Interpretability in Random Forests
Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig Maximilian University of Munich (LMU)（慕尼黑路德维希-马克西米利安大学医学信息处理与流行病学研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Institute of General Practice and Health Services Research, Department Clinical Medicine, TUM School of Medicine and Health, Technical University of Munich (TUM)（慕尼黑技术大学医学与健康学院一般医学与健康服务研究研究所）；Institute of AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich (TUM)（慕尼黑技术大学医学与健康学院医学人工智能与信息学研究所）
