In this study, a new Stacked Generalization technique called Fuzzy Stacked Generalization (FSG) is proposed to minimize the difference between N -sample and large-sample classification error of the Nearest Neighbor classifier. The proposed FSG employs a new hierarchical distance learning strategy to minimize the error difference. For this purpose, we first construct an ensemble of base-layer fuzzy k- Nearest Neighbor (k-NN) classifiers, each of which receives a different feature set extracted from the same sample set. The fuzzy membership values computed at the decision space of each fuzzy k-NN classifier are concatenated to form the feature vectors of a fusion space. Finally, the feature vectors are fed to a meta-layer classifier to learn the degree of accuracy of the decisions of the base-layer classifiers for meta-layer classification. Rather than the power of the individual base layer-classifiers, diversity and cooperation of the classifiers become an important issue to improve the overall performance of the proposed FSG. A weak base-layer classifier may boost the overall performance more than a strong classifier, if it is capable of recognizing the samples, which are not recognized by the rest of the classifiers, in its own feature space. The experiments explore the type of the collaboration among the individual classifiers required for an improved performance of the suggested architecture. Experiments on multiple feature real-world datasets show that the proposed FSG performs better than the state of the art ensemble learning algorithms such as Adaboost, Random Subspace and Rotation Forest. On the other hand, compatible performances are observed in the experiments on single feature multi-attribute datasets.
AntiPhishStack: LSTM-based Stacked Generalization Model for Optimized Phishing URL Detection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences（中国科学院大学深圳先进技术学院）；University of Genoa（热那亚大学）；University of Catania（卡塔尼亚大学）；Shenzhen Polytechnic University（深圳职业技术大学）
Stacked Generalization for Human Activity Recognition
Center for Pattern Analysis and Machine Learning Dept. of Electrical and Computer Engineering University of Waterloo（滑铁卢大学电气与计算机工程系模式分析与机器学习中心）；University of Waterloo（滑铁卢大学）
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；TECNALIA, Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA）泰克纳利亚研究中心）；Technical University of Munich（慕尼黑工业大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Alberta（阿尔伯塔大学）；The Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
Department of Electrical and Computer Engineering, University of Alberta（阿尔伯塔大学电气与计算机工程系）；Department of Computer Science and Technology, Nanjing University（南京大学计算机科学与技术系）；School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；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）
