In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

不平衡的数据（ID）是阻止机器学习（ML）模型以实现令人满意的结果的问题。 ID是一种情况，即属于一个类别的样本的数量超过另一个类别的情况，这使此类模型学习过程偏向多数类。近年来，为了解决这个问题，已经提出了几种解决方案，该解决方案选择合成为少数族裔类生成新数据，或者减少平衡数据的多数类的数量。因此，在本文中，我们研究了基于深神经网络（DNN）和卷积神经网络（CNN）的方法的有效性，并与各种众所周知的不平衡数据解决方案混合，这意味着过采样和降采样。为了评估我们的方法，我们使用了龙骨，乳腺癌和Z-Alizadeh Sani数据集。为了获得可靠的结果，我们通过随机洗牌的数据分布进行了100次实验。分类结果表明，混合的合成少数族裔过采样技术（SMOTE） - 正态化-CNN优于在24个不平衡数据集上达到99.08％精度的不同方法。因此，提出的混合模型可以应用于其他实际数据集上的不平衡算法分类问题。

深度学习模型记住培训数据，这损害了他们推广到代表性不足的课程的能力。我们从经验上研究了卷积神经网络对图像数据不平衡数据的内部表示，并测量了训练和测试集中模型特征嵌入之间的概括差距，这表明该差距对于少数类别的差异更大。这个洞察力使我们能够为不平衡数据设计有效的三相CNN培训框架。该框架涉及训练网络端到端的数据不平衡数据以学习准确的功能嵌入，在学习的嵌入式空间中执行数据增强以平衡火车分布，并在嵌入式平衡的培训数据上微调分类器头。我们建议在培训框架中使用广泛的过采样（EOS）作为数据增强技术。 EOS形成合成训练实例，作为少数族类样本与其最近的敌人之间的凸组合，以减少概括差距。提出的框架提高了与不平衡学习中常用的领先成本敏感和重新采样方法的准确性。此外，它比标准数据预处理方法（例如SMOTE和基于GAN的过采样）更有效，因为它需要更少的参数和更少的训练时间。

无线电星系的连续排放通常可以分为不同的形态学类，如FRI，Frii，弯曲或紧凑。在本文中，我们根据使用深度学习方法使用小规模数据集的深度学习方法来探讨基于形态的无线电星系分类的任务（$ \ SIM 2000 $ Samples）。我们基于双网络应用了几次射击学习技术，并使用预先培训的DENSENET模型进行了先进技术的传输学习技术，如循环学习率和歧视性学习迅速训练模型。我们使用最佳表演模型实现了超过92 \％的分类准确性，其中最大的混乱来源是弯曲和周五型星系。我们的结果表明，专注于一个小但策划数据集随着使用最佳实践来训练神经网络可能会导致良好的结果。自动分类技术对于即将到来的下一代无线电望远镜的调查至关重要，这预计将在不久的将来检测数十万个新的无线电星系。

在过去的几年中，卷积神经网络（CNN）占据了计算机视野的领域，这要归功于它们提取功能及其在分类问题中出色的表现，例如在自动分析X射线中。不幸的是，这些神经网络被认为是黑盒算法，即不可能了解该算法如何实现最终结果。要将这些算法应用于不同领域并测试方法论的工作原理，我们需要使用可解释的AI技术。医学领域的大多数工作都集中在二进制或多类分类问题上。但是，在许多现实生活中，例如胸部X射线射线，可以同时出现不同疾病的放射学迹象。这引起了所谓的“多标签分类问题”。这些任务的缺点是类不平衡，即不同的标签没有相同数量的样本。本文的主要贡献是一种深度学习方法，用于不平衡的多标签胸部X射线数据集。它为当前未充分利用的Padchest数据集建立了基线，并基于热图建立了可解释的AI技术。该技术还包括概率和模型间匹配。我们系统的结果很有希望，尤其是考虑到使用的标签数量。此外，热图与预期区域相匹配，即它们标志着专家将用来做出决定的区域。

通过卫星摄像机获取关于地球表面的大面积的信息使我们能够看到远远超过我们在地面上看到的更多。这有助于我们在检测和监测土地使用模式，大气条件，森林覆盖和许多非上市方面的地区的物理特征。所获得的图像不仅跟踪连续的自然现象，而且对解决严重森林砍伐的全球挑战也至关重要。其中亚马逊盆地每年占最大份额。适当的数据分析将有助于利用可持续健康的氛围来限制对生态系统和生物多样性的不利影响。本报告旨在通过不同的机器学习和优越的深度学习模型用大气和各种陆地覆盖或土地使用亚马逊雨林的卫星图像芯片。评估是基于F2度量完成的，而用于损耗函数，我们都有S形跨熵以及Softmax交叉熵。在使用预先训练的ImageNet架构中仅提取功能之后，图像被间接馈送到机器学习分类器。鉴于深度学习模型，通过传输学习使用微调Imagenet预训练模型的集合。到目前为止，我们的最佳分数与F2度量为0.927。

疾病预测是医学应用中的知名分类问题。 GCNS提供了一个强大的工具，用于分析患者相对于彼此的特征。这可以通过将问题建模作为图形节点分类任务来实现，其中每个节点是患者。由于这种医学数据集的性质，类别不平衡是疾病预测领域的普遍存在问题，其中类的分布是歪曲的。当数据中存在类别不平衡时，现有的基于图形的分类器倾向于偏向于主要类别并忽略小类中的样本。另一方面，所有患者中罕见阳性病例的正确诊断在医疗保健系统中至关重要。在传统方法中，通过将适当的权重分配给丢失函数中的类别来解决这种不平衡，这仍然依赖于对异常值敏感的权重的相对值，并且在某些情况下偏向于小类（ES）。在本文中，我们提出了一种重加权的对抗性图形卷积网络（RA-GCN），以防止基于图形的分类器强调任何特定类的样本。这是通过将基于图形的神经网络与每个类相关联来完成的，这负责加权类样本并改变分类器的每个样本的重要性。因此，分类器自身调节并确定类之间的边界，更加关注重要样本。分类器和加权网络的参数受到侵犯方法训练。我们在合成和三个公共医疗数据集上显示实验。与最近的方法相比，ra-gcn展示了与最近的方法在所有三个数据集上识别患者状态的方法相比。详细分析作为合成数据集的定量和定性实验提供。

根据研究人员在歧视和校准性能方面采用的标准评估实践，这项工作旨在了解阶级不平衡对胸部X射线分类器的性能的影响。首先，我们进行了一项文献研究，分析了普通科学实践并确认：（1）即使在处理高度不平衡的数据集时，社区也倾向于使用由大多数阶级主导的指标; （2）包括包括胸部X射线分类器的校准研究仍然罕见，尽管其在医疗保健的背景下的重要性。其次，我们对两个主要胸部X射线数据集进行了系统实验，探讨了不同类别比率下的几种性能指标的行为，并显示了广泛采用的指标可以隐藏少数阶级中的性能。最后，我们提出了通过两个替代度量，精密召回曲线和平衡的Brier得分，这更好地反映了系统在这种情况下的性能。我们的研究结果表明，胸部X射线分类器研究界采用的当前评估实践可能无法反映真实临床情景中计算机辅助诊断系统的性能，并建议改善这种情况的替代方案。

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.

从不平衡数据中学习是一项具有挑战性的任务。在进行不平衡数据训练时，标准分类算法的性能往往差。需要通过修改数据分布或重新设计基础分类算法以实现理想的性能来采用一些特殊的策略。现实世界数据集中不平衡的流行率导致为班级不平衡问题创造了多种策略。但是，并非所有策略在不同的失衡情况下都有用或提供良好的性能。处理不平衡的数据有许多方法，但是尚未进行此类技术的功效或这些技术之间的实验比较。在这项研究中，我们对26种流行抽样技术进行了全面分析，以了解它们在处理不平衡数据方面的有效性。在50个数据集上进行了严格的实验，具有不同程度的不平衡，以彻底研究这些技术的性能。已经提出了对技术的优势和局限性的详细讨论，以及如何克服此类局限性。我们确定了影响采样策略的一些关键因素，并提供有关如何为特定应用选择合适的采样技术的建议。

类不平衡是分类任务中经常发生的情况。从不平衡数据中学习提出了一个重大挑战，这在该领域引起了很多研究。使用采样技术进行数据预处理是处理数据中存在的不平衡的标准方法。由于标准分类算法在不平衡数据上的性能不佳，因此在培训之前，数据集需要足够平衡。这可以通过过度采样少数族裔级别或对多数级别的采样来实现。在这项研究中，已经提出了一种新型的混合采样算法。为了克服采样技术的局限性，同时确保保留采样数据集的质量，已经开发了一个复杂的框架来正确结合三种不同的采样技术。首先应用邻里清洁规则以减少失衡。然后从策略上与SMOTE算法策略性地采样，以在数据集中获得最佳平衡。该提出的混合方法学称为“ smote-rus-nc”，已与其他最先进的采样技术进行了比较。该策略进一步合并到集合学习框架中，以获得更健壮的分类算法，称为“ SRN-BRF”。对26个不平衡数据集进行了严格的实验，并具有不同程度的失衡。在几乎所有数据集中，提出的两种算法在许多情况下都超过了现有的采样策略，其差额很大。尤其是在流行抽样技术完全失败的高度不平衡数据集中，他们实现了无与伦比的性能。获得的优越结果证明了所提出的模型的功效及其在不平衡域中具有强大采样算法的潜力。

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes. We design two novel methods to improve performance in such scenarios. First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound. This loss replaces the standard cross-entropy objective during training and can be applied with prior strategies for training with class-imbalance such as re-weighting or re-sampling. Second, we propose a simple, yet effective, training schedule that defers re-weighting until after the initial stage, allowing the model to learn an initial representation while avoiding some of the complications associated with re-weighting or re-sampling. We test our methods on several benchmark vision tasks including the real-world imbalanced dataset iNaturalist 2018. Our experiments show that either of these methods alone can already improve over existing techniques and their combination achieves even better performance gains 1 .

深度神经网络（DNN）对于对培训期间的样品大大减少的课程进行更多错误是臭名昭着的。这种类别不平衡在临床应用中普遍存在，并且对处理非常重要，因为样品较少的类通常对应于临界病例（例如，癌症），其中错误分类可能具有严重后果。不要错过这种情况，通过设定更高的阈值，需要以高真正的阳性率（TPRS）运行二进制分类器，但这是类别不平衡问题的非常高的假阳性率（FPRS）的成本。在课堂失衡下的现有方法通常不会考虑到这一点。我们认为，通过在高TPRS处于阳性的错误分类时强调减少FPRS，应提高预测准确性，即赋予阳性，即批判性，类样本与更高的成本相关。为此，我们将DNN的训练训练为二进制分类作为约束优化问题，并引入一种新的约束，可以通过在高TPR处优先考虑FPR减少来强制ROC曲线（AUC）下强制实施最大面积的新约束。我们使用增强拉格朗日方法（ALM）解决了由此产生的受限优化问题。超越二进制文件，我们还提出了两个可能的延长了多级分类问题的建议约束。我们使用内部医学成像数据集，CIFAR10和CIFAR100呈现基于图像的二元和多级分类应用的实验结果。我们的结果表明，该方法通过在关键类别的准确性上获得了大多数病例的拟议方法，同时降低了非关键类别样本的错误分类率。

X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

为了确保全球粮食安全和利益相关者的总体利润，正确检测和分类植物疾病的重要性至关重要。在这方面，基于深度学习的图像分类的出现引入了大量解决方案。但是，这些解决方案在低端设备中的适用性需要快速，准确和计算廉价的系统。这项工作提出了一种基于轻巧的转移学习方法，用于从番茄叶中检测疾病。它利用一种有效的预处理方法来增强具有照明校正的叶片图像，以改善分类。我们的系统使用组合模型来提取功能，该模型由预审计的MobilenETV2体系结构和分类器网络组成，以进行有效的预测。传统的增强方法被运行时的增加取代，以避免数据泄漏并解决类不平衡问题。来自PlantVillage数据集的番茄叶图像的评估表明，所提出的体系结构可实现99.30％的精度，型号大小为9.60mb和4.87亿个浮点操作，使其成为低端设备中现实生活的合适选择。我们的代码和型号可在https://github.com/redwankarimsony/project-tomato中找到。

数据不平衡，即来自不同课程的培训观测数量之间的歧视，仍然是影响当代机器学习的最重要挑战之一。数据预处理技术可以减少数据不平衡对传统分类算法的负面影响，可以减少操纵训练数据以人为地降低不平衡程度的方法。然而，现有的数据预处理技术，特别是粉迹及其衍生物构成最普遍的数据预处理的范式，往往易于各种数据难度因素。这部分是由于原始粉碎算法不利用有关多数类观察的信息的事实。本文的重点是利用少数群体和多数阶级的分布的信息，自然地发展新的数据重采样策略。本文总结了12个研究论文的内容，专注于所提出的二进制数据重采采样策略，它们与多级环境的翻译，以及对组织病理数据分类问题的实际应用。

With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes and the essentially gradual changes from one morphological class to the adjacent class (DDRGC). These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, ``Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing" (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) Designed a hierarchical galaxy classification model based on an efficient backbone network; (2) Utilized a weighted sampling scheme to deal with the imbalance problem; (3) Adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy is 96.32\%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme.

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-ofthe-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.

使用卷积神经网络（CNN）已经显着改善了几种图像处理任务，例如图像分类和对象检测。与Reset和Abseralnet一样，许多架构在创建时至少在一个数据集中实现了出色的结果。培训的一个关键因素涉及网络的正规化，这可以防止结构过度装备。这项工作分析了在过去几年中开发的几种正规化方法，显示了不同CNN模型的显着改进。该作品分为三个主要区域：第一个称为“数据增强”，其中所有技术都侧重于执行输入数据的更改。第二个，命名为“内部更改”，旨在描述修改神经网络或内核生成的特征映射的过程。最后一个称为“标签”，涉及转换给定输入的标签。这项工作提出了与关于正则化的其他可用调查相比的两个主要差异：（i）第一个涉及在稿件中收集的论文并非超过五年，并第二个区别是关于可重复性，即所有作品此处推荐在公共存储库中可用的代码，或者它们已直接在某些框架中实现，例如Tensorflow或Torch。

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}