Overweighting samples of such classes can lead to drop in the model's overall performance. We claim that the 'difficulty' of a class as perceived by the model is more important to determine the weighting. In this light, we propose a novel loss function named Class-wise Difficulty-Balanced ...
最近在做深度学习的多分类问题,主要遇到class imbalance类别不均衡(在我的数据集中最多的类别有超过1.5w的样本数,还有相当多的类只有几十、几百样本数)、类别数量众多(在我的数据集中共有30w左右的样本,需要分530类),在做之前一度怀疑面对如此大的类别数量、较大的类别不均衡问题,自己的模型能否handle,也拖了好久...
balanced-fl/Addressing-Class-Imbalance-FL Star82 Code Issues Pull requests This is the code for Addressing Class Imbalance in Federated Learning (AAAI-2021). machine-learningclass-imbalanceloss-functionsfederated-learningimbalance-classificationneural-network-training ...
第一种方法:loss函数构造---focal loss 及其魔改 1.1 Imbalance deep multi‐instance learning for predicting isoform–isoform interactions (2020发的, 2021年接收) code:http://mlda.swu.edu.cn/codes.php?name=IDMIL‐III 在本文中,我们提出了一种不平衡的深度多实例学习方法(IDMIL-III),并将其应用于预测...
distribution. Therefore, new techniques that do not rely on class distribution should be further explored, such as focal loss (Lin et al., 2017) and adaptive class suppression loss (Wang et al., 2021). In addition, GANs can also be utilized to address the class imbalance by generating ...
类别不平衡(class-imbalance)就是指分类任务中不同类别的训练样例数目差别很大的情况。如果不同类别的训练样例数目稍有差别,通常影响不大,但若差别很大,则会对学习过程造成困扰。例如有998个反例,但是正例只有2个,那么学习方法只需要返回一个永远将新样本预测为反例的学习器,就能达到99.8%的精度;然而这样的学习器往...
Many of the described churn prediction algorithms can be applied to other scenarios, for example customer targeting prediction (Coussement, Harrigan, & Benoit, 2015) or yes/no recommendation prediction. With churn data, there can be a strongclass imbalance problem, with only a few churners and...
On long-tailed CIFAR dataset (the hyperparameterIM_FACTORis the inverse of "Imbalance Factor" in the paper): ./cifar_im_trainval.sh On long-tailed CIFAR dataset using the proposed class-balanced loss (set non-zeroBETA): ./cifar_im_trainval_cb.sh ...
The focal loss is designed to address class imbalance by down-weighting inliers (easy examples) such that their contribution to the total loss is small even if their number is large. It focuses on training a sparse set of hard examples. ...
class imbalance in multi-label classification, causing models to favor majority classes and overlook minority classes during training. Additionally, traditional feature extraction methods have high computational costs, incomplete features, and may lead to the loss of critical information. On the other ...