A Novel Framework for Optimised Ensemble Classifier

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，以改进数据分析和许多现实世界应用程序的准确性，如文档分析、机器人和医疗诊断。该项目计划开发和研究产生不同训练环境层、基本分类器和分类器融合的新方法。它还计划设计一种基于多目标进化算法的搜索方法，以获得最优的层数、聚类数和基分类数。提出的框架的预期结果是分类器学习的进步。最终的结果可能是新的方法，这些方法将在基本分类器的学习过程中带来多样性，并为现实世界的应用提供最佳的集成分类器。

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.