Comprehensive Comparative Study of Multi-Label Classification Methods
Technical University Berlin（柏林工业大学）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；University of Ljubljana（卢布尔雅那大学）；Faculty of Public Administration, University of Ljubljana（卢布尔雅那大学公共管理学院）；Bias Variance Labs, d.o.o.（Bias Variance Labs有限责任公司）
Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods. However, they are limited in the number of methods and datasets considered. This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains. More specifically, our study evaluates 26 methods on 42 benchmark datasets using 20 evaluation measures. The adopted evaluation methodology adheres to the highest literature standards for designing and executing large scale, time-budgeted experimental studies. First, the methods are selected based on their usage by the community, assuring representation of methods across the MLC taxonomy of methods and different base learners. Second, the datasets cover a wide range of complexity and domains of application. The selected evaluation measures assess the predictive performance and the efficiency of the methods. The results of the analysis identify RFPCT, RFDTBR, ECCJ48, EBRJ48 and AdaBoostMH as best performing methods across the spectrum of performance measures. Whenever a new method is introduced, it should be compared to different subsets of MLC methods, determined on the basis of the different evaluation criteria.
Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
University of Wisconsin, Madison（威斯康星大学麦迪逊分校）；University of Washington（华盛顿大学）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Riverside（加州大学河滨分校）
MoCap-Impute: A Comprehensive Benchmark and Comparative Analysis of Imputation Methods for IMU-based Motion Capture Data
Peter Faber Business School, Australian Catholic University（澳大利亚天主教大学彼得·法伯商学院）；University of Technology Sydney（技术与应用科学大学悉尼分校）；Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences（技术与应用科学大学信息科技系）；Faculty of Computers and Informatics, Zagazig University（扎基尔大学计算机与信息学院）；AI Applications Research Chair, University of Nizwa（努孜瓦大学人工智能应用研究 chair）；Department of Data Science, Wakeb（数据科学系，Wakeb）；Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University（计算机科学系，普林斯·萨塔姆·本·阿卜杜勒阿齐兹大学）；Higher Future Institute for Specialized Technological Studies, Cairo, Egypt（埃及未来高级专门技术研究学院）；Department of Computer Science, Faculty of Computers & Informatics, Suez Canal University（计算机科学系，苏伊士运河大学）；Department of Mathematics, Faculty of Science, Suez Canal University（数学系，苏伊士运河大学）
Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting
Czech Technical University in Prague（布拉格捷克技术大学）；Faculty of Information Technology（信息技术学院）；Brno University of Technology（布尔诺理工大学）；CESNET, a.l.e.（CESNET公司）
Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations
School of Science；Engineering, Al Akhawayn University, Ifrane, Morocco.；Engineering Sciences Laboratory,Polydisciplinary Faculty of Taza, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；Signal Systems \& Components Laboratory, Faculty of Sciences；Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco.；University of Medina Saudi Arabia, Kingdom of Saudi Arabia
A comparative analysis of rank aggregation methods for the partial label ranking problem
Laboratorio de Sistemas Inteligentes y Minería de Datos, Universidad de Castilla-La Mancha, Albacete, Spain（智能系统与数据挖掘实验室，卡斯蒂利亚-拉曼查大学，阿尔巴塞特，西班牙）；German Research Center for Artificial Intelligence, Kaiserslautern, Germany（德国人工智能研究中心，凯泽斯劳滕，德国）
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Simula Metropolitan Center for Digital Engineering（Simula 数字工程中心）；Oslo Metropolitan University（奥斯陆 Metropolitan 大学）；University of Oslo（奥斯陆大学）；Simula Research Laboratory（Simula 研究实验室）；Norwegian Directorate of Health（挪威健康 Directorate）
School of Computer Science and Technology, Harbin Institute of Technology,Shenzhen（计算机科学与技术学院，哈尔滨工业大学，深圳）；Inspur Cloud Information Technology Co., Ltd（Inspur 云信息科技有限公司）
