  In supervised learning, the presence of noise can have a significant impact
on decision making. Since many classifiers do not take label noise into account
in the derivation of the loss function, including the loss functions of
logistic regression, SVM, and AdaBoost, especially the AdaBoost iterative
algorithm, whose core idea is to continuously increase the weight value of the
misclassified samples, the weight of samples in many presence of label noise
will be increased, leading to a decrease in model accuracy. In addition, the
learning process of BP neural network and decision tree will also be affected
by label noise. Therefore, solving the label noise problem is an important
element of maintaining the robustness of the network model, which is of great
practical significance. Granular ball computing is an important modeling method
developed in the field of granular computing in recent years, which is an
efficient, robust and scalable learning method. In this paper, we pioneered a
granular ball neural network algorithm model, which adopts the idea of
multi-granular to filter label noise samples during model training, solving the
current problem of model instability caused by label noise in the field of deep
learning, greatly reducing the proportion of label noise in training samples
and improving the robustness of neural network models.
