University of New South Wales（新南威尔士大学）；UNSW Data Science Hub（新南威尔士大学数据科学中心）；Australian Research Council - Industrial Transformation Training Centre（澳大利亚研究委员会工业转型培训中心）
Giang Ngo, Rodney Beard, Rohitash Chandra
发表信息：
Neurocomputing, Volume 510, 21 October 2022, Pages 1-14
Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example of bagging with additional features in the learning process. Evolutionary algorithms have been prominent for optimisation problems and also been used for machine learning. Evolutionary algorithms are gradient-free methods that work with a population of candidate solutions that maintain diversity for creating new solutions. In conventional bagged ensemble learning, the bags are created once and the content, in terms of the training examples, are fixed over the learning process. In our paper, we propose evolutionary bagged ensemble learning, where we utilise evolutionary algorithms to evolve the content of the bags in order to iteratively enhance the ensemble by providing diversity in the bags. The results show that our evolutionary ensemble bagging method outperforms conventional ensemble methods (bagging and random forests) for several benchmark datasets under certain constraints. We find that evolutionary bagging can inherently sustain a diverse set of bags without reduction in performance accuracy.
Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models
School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models
Organization for the Promotion of Education, Yokohama National University（横滨国立大学教育推进机构）；School of Engineering, Westlake University（西湖大学工学院）；Faculty of Engineering, Yokohama National University（横滨国立大学工学部）
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
Research Institute of Trustworthy Autonomous System, Southern University of Science and Technology（南方科技大学可信自主系统研究院）；Trustworthiness Theory Research Center, Huawei Technologies Co., Ltd.（华为技术有限公司可信理论研究中心）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）
