A Novel Framework for Optimised Ensemble Classifier

优化集成分类器的新框架

• 批准号: DP160102639
• 负责人: Prof Brijesh Verma
• 金额: $ 18.69万
• 依托单位: Central Queensland University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-01-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160102639
• 关键词: Novel; Framework; Optimised; Ensemble; Classifier

The project aims to develop a novel framework for creating an optimised ensemble classifier that will improve data analysis and the accuracy of many real-world applications such as document analysis, robotics and medical diagnosis. The project plans to develop and investigate novel methods for generating diverse training environment layers, base classifiers and fusion of classifiers. It also plans to design a multi-objective evolutionary algorithm-based search obtain the optimal number of layers, clusters and base classifiers. The expected outcomes of the proposed framework are advances in classifier learning. The final outcome may be novel methods which will bring in diversity during the learning of the base classifiers and provide an optimal ensemble classifier for real-world applications.

该项目旨在开发一种新的框架，用于创建优化的集成分类器，以改善数据分析和许多现实世界应用的准确性，如文件分析，机器人和医疗诊断。该项目计划开发和研究用于生成不同训练环境层、基础分类器和分类器融合的新方法。设计了一种基于多目标进化算法的搜索算法，获得最优的层数、聚类数和基分类器数。所提出的框架的预期成果是分类器学习的进步。最终的结果可能是新的方法，这将带来在学习的基础分类器的多样性，并提供一个最佳的集成分类器，为现实世界的应用。