Developing and improving methods for robust ensemble classification: an aggregation operator and clustering-classification approach - Agrawal, Utkarsh (2019)
Classification is an important technique (in pattern recognition) to categorise objects within their respective groups. In most real-world pattern recognition problems, it has become difficult to achieve best performance using an individual classifier. Ensemble algorithms, which are methods combining multiple individual classifiers, have already earned widespread approval within the machine learning community due to their ability to produce results in a wide range of applications. However, some challenges still exist in order to achieve a robust classification, in particular, classification of data points which are difficult to be assigned in one of the groups, and leveraging of existing external knowledge in order to better combine individual classifier outputs (fusion step of the ensemble). This thesis comprehensively explores these two key aspects and issues among the ensemble methods.