LAUSR

Label noise is a common problem that affects supervised learning and can produce misleading results. It is shown that only $$5\,\%$$5% of switched labels lead to a decrease of performances. Therefore, the true class of an instance must be distinguished from its observed label. In the past decade, classification in presence of label noise was the topic of interest. Several scholars focused on kNN-based approaches for data cleansing. These types of approaches often are susceptible to high label noise rate and when a batch of instances with noisy labels are exist they may deteriorate the results. The problem arises since the methods have a local view of instances. Another approach is to have a global view of instances. In a global view, instances with large distance from their respective classes are detected as noisy. A potential problem however is the determination of a threshold. An inappropriate threshold may lead to detection of a correct instance as noisy instance. In this paper a new method for label denoising based on Bayesian aggregation is proposed which solves the problems of kNN-based approaches by aggregating the local and global views of instances. The aggregation of local and global information leads to a more robust and accurate detection of instances with noisy labels and estimation of their true labels. The experimental results show the capabilities and robustness of the proposed method.