Multi-class granular approximation by means of disjoint and adjacent fuzzy granules
Ghent University（根特大学）；University of Catania（卡塔尼亚大学）；University of Portsmouth（朴茨茅斯大学）；Poznań University of Technology（波兹南理工大学）；Polish Academy of Sciences（波兰科学院）
Marko Palangetić, Chris Cornelis, Salvatore Greco, Roman Słowiński
In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w.r.t. a given closeness measure. Such sets are called granular approximations. In this article, we introduce the concepts of disjoint and adjacent granules and we examine how the new definitions affect the granular approximations. First, we show that the new concepts are important for binary classification problems since they help to keep decision regions separated (disjoint granules) and at the same time to cover as much as possible of the attribute space (adjacent granules). Later, we consider granular approximations for multi-class classification problems leading to the definition of a multi-class granular approximation. Finally, we show how to efficiently calculate multi-class granular approximations for Łukasiewicz fuzzy connectives. We also provide graphical illustrations for a better understanding of the introduced concepts.
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Department of Electrical and Computer Engineering, King Abdulaziz University（阿卜杜勒阿齐兹大学电气与计算机工程系）；Department of Computer Engineering, Istinye University（伊斯提尼耶大学计算机工程系）
Ghent University（根特大学）；University of Catania（卡塔尼亚大学）；University of Portsmouth（朴茨茅斯大学）；Poznań University of Technology（波兹南理工大学）；Polish Academy of Sciences（波兰科学院）
FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification
School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；School of Computer Science and Technology, Nantong University（南通大学计算机科学与技术学院）；Centre for Artificial Intelligence, FEIT, University of Technology Sydney（悉尼科技大学工程与信息技术学院人工智能中心）
Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector
School of Electro-Mechanical Engineering, Xidian University（西安电子科技大学机电工程学院）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Faculty of Engineering, King Abdulaziz University（阿卜杜勒阿齐兹大学工程学院）
