Meta-learning-based sample discrimination framework for improving dynamic selection of classifiers under label noise

Many real-world datasets encounter the issue of label noise (LN), which significantly degrades the learning performances of classification models. While ensemble learning (EL) has been widely employed to tackle this problem, the Dynamic Selection (DS) of classifiers, as a promising EL branch, is particularly sensitive to LN. To address this issue, a meta-learning-based sample discrimination (MSD) framework is proposed in this paper. Initially, this paper analyzes how LN affects the performance of DS methods through a visual example. Subsequently, under the premise that DS methods are only applicable to samples whose neighborhood is minimally affected or unaffected by LN, a meta-learning dataset is generated in the framework, where the meta-features and meta-labels are derived from the characteristics and the real class distribution of local regions of the samples, respectively. With this dataset, a meta-learner is constructed to determine the feasibility of using DS methods directly to classify a given sample in the presence of LN. For samples that DS methods cannot handle, a novel DS process based on the Genetic Algorithm is designed to mitigate the negative impact of LN. The effectiveness of the MSD framework is validated through extensive experiments conducted on thirty real datasets. These experiments demonstrate the capability of the MSD framework to improve the performances of DS methods across different levels of LN. Furthermore, the efficacy of the proposed MSD framework in handling LN is also highlighted by comparing it with a state-of-the-art method and four mainstream EL methods.