论文标题
基于粒状球计算的深层CNN模型的研究
A Study of Deep CNN Model with Labeling Noise Based on Granular-ball Computing
论文作者
论文摘要
在监督的学习中，噪声的存在可能会对决策产生重大影响。由于许多分类器在推导损失功能的推导中没有考虑到标签噪声，包括逻辑回归，SVM和Adaboost的损失函数，尤其是Adaboost迭代算法，其核心思想是，其核心思想是在误导性噪声中不断增加样本的重量，在许多存在标记的噪声中会增加模型的降低，从而使模型降低了一个模型的精神降低。此外，BP神经网络和决策树的学习过程也将受标签噪声的影响。因此，解决标签噪声问题是维持网络模型鲁棒性的重要组成部分，这具有极大的实际意义。粒状球计算是近年来在颗粒计算领域开发的一种重要的建模方法，这是一种有效，健壮和可扩展的学习方法。在本文中，我们开创了一个颗粒状的ball神经网络算法模型，该模型在模型训练过程中采用了多个粒度到过滤标签的噪声样本的概念，解决了当前由深度学习领域的标签噪声引起的模型不稳定性问题，从而大大降低了训练样本中标签噪声的比例，并改善了神经网络模型的稳健性。
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.
