Loss functionOptimizationnn-UnetImage segmentation in the medical domain has gained a lot of research interest in recent years with the advancements in deep learning algorithms and related technologies. Medical image datasets are often imbalanced and to handle the imbalance problem, deep learning models...
以往解决 class imbalance 的方法可以分为三类:data-level、algorithm-level、hybrid methods 1. data-level:data re-sampling、data augmentation 2. algorithm-level:meta-learning、model tuning、cost-sensitive learning、changing the loss function 3. hybrid methods:ensemble learning 由于数据隐私问题,data-level ...
(2016) explore the class imbalance problem in the contexts of images and text documents, focusing on problems related to the loss function used in MLPs. In their experiments, three different imbalance ratios were examined for both contexts. Namely, the ratio between the minority and the majority...
Class imbalance is a common problem that often occurs in multi-label image classification. In multi-label datasets, the co-occurrence of labels presents a ... Y Zhang,S Cao,S Mi,... - 《Pattern Analysis & Applications Paa》 被引量: 0发表: 2024年 Definition of loss functions for learning...
By adjusting the weight parameters in the loss function of the SVM model, we can reduce class imbalance by assigning higher weights to the minority class examples and making the model pay more attention to minority samples. In order to find the best combination of weights, we implemented cost-...
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...
The study's authors also proposed a novel loss function called "Categorical Dice" and implemented a strategy of assigning varying weights to distinct segmented regions concurrently. This approach effectively addressed the issue of voxel imbalance. The primary issue with cascaded CNNs is that they ...
with some classes having much larger quantities of rna than others. this imbalance causes models to favor majority classes and overlook minority classes. the improved focal loss function increases the weight of minority classes, making the model focus more on these hard-to-classify samples during tr...
the data imbalance between the seen and unseen classes in the LSL setting. Finally, preference was given to works which offer novel solutions or analysis on the overlap between LSL and CI; thus, we often excluded works that simply apply trivial techniques (e.g., a modified loss function) ...
The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-wor