在表格数据上构建分类器时处理类别分布不平衡，长期以来一直是一个受到关注的问题。一种流行方法是使用合成生成的数据增强训练数据集。经典增强技术仅限于对现有少数类样本进行线性插值，而近期容量更高的深度生成模型正展现出更大前景。然而，在构建深度生成模型时处理类别分布不平衡同样是一个具有挑战性的问题，其研究程度尚不及不平衡分类器模型训练。我们表明，最先进的深度生成模型所生成的少数类样本质量明显低于多数类样本。我们提出一种新技术，通过为少数类与多数类分布重叠的区域引入一个类别，将二分类标签转换为三分类标签。我们表明，仅仅对训练集进行这种预处理，就能够显著提升跨多种最先进的扩散模型和基于 GAN 的模型所生成数据的质量。在使用合成数据训练分类器时，我们从训练数据中移除重叠类别，并论证准确率提升背后的原因。我们在四个真实数据集、五种不同分类器和五种生成模型上进行了大量实验，结果表明我们的方法不仅提升了最先进模型的合成器性能，也提升了分类器性能。
英文摘要：
Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacity deep generative models are providing greater promise.
However, handling of imbalance in class distribution when building a deep generative model is also a challenging problem, that has not been studied as extensively as imbalanced classifier model training. We show that state-of-the-art deep generative models yield significantly lower-quality minority examples than majority examples. %In this paper, we start with the observation that imbalanced data training of generative models trained imbalanced dataset which under-represent the minority class. We propose a novel technique of converting the binary class labels to ternary class labels by introducing a class for the region where minority and majority distributions overlap. We show that just this pre-processing of the training set, significantly improves the quality of data generated spanning several state-of-the-art diffusion and GAN-based models. While training the classifier using synthetic data, we remove the overlap class from the training data and justify the reasons behind the enhanced accuracy. We perform extensive experiments on four real-life datasets, five different classifiers, and five generative models demonstrating that our method enhances not only the synthesizer performance of state-of-the-art models but also the classifier performance.
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minority class. Standard methods like SMOTE rely on finding nearest neighbours and linear interpolations which are problematic in case of high-dimensional, complex data distributions. Generative Adversarial Networks (GANs) have been proposed as an alternative method for generating artificial minority examples as they can model complex distributions. However, prior research on GAN-based oversampling does not incorporate rece…
Tel Aviv University（特拉维夫大学）；Playtika Ltd.（Playtika公司）；Bar-Ilan University（巴伊兰大学）
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. This translation mechanism encourages the synthesized samples to be close to the class boundary. Furthermore, we explore a selection criterion to retain the most useful of the synthesized samples. Experimental results using several downstream cl…
Although deep learning has achieved impressive advances in transient stability assessment of power systems, the insufficient and imbalanced samples still trap the training effect of the data-driven methods. This paper proposes a controllable sample generation framework based on Conditional Tabular Generative Adversarial Network (CTGAN) to generate specified transient stability samples. To fit the complex feature distribution of the transient stability samples, the proposed framework firstly models the samples as tabular data and uses Gaussian mixture models to normalize the tabular data. Then …
Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software. Thus, there are numerous techniques to avoid malware, one of which is to develop samples of malware so that the system can be updated with the growing number of malwares, allowing it to recognize when malwares attempt to enter. The Generative Adversaria…
Synthesizing tabular data is attracting much attention these days for various purposes. With sophisticate synthetic data, for instance, one can augment its training data. For the past couple of years, tabular data synthesis techniques have been greatly improved. Recent work made progress to address many problems in synthesizing tabular data, such as the imbalanced distribution and multimodality problems. However, the data utility of state-of-the-art methods is not satisfactory yet. In this work, we significantly improve the utility by designing our generator and discriminator based on neural o…
MIT LIDS（麻省理工学院信息与决策系统实验室）；MRC-BSU, University of Cambridge（剑桥大学MRC生物统计组）；Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）
Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult. Existing statistical and deep neural network models fail to properly model this type of data. We design TGAN, which uses a conditional generative adversarial network to address these challenges. To aid in a fair and thorough comparison, we design a benchmark with 7 simulated and…
Zhejiang University（浙江大学）；Zhejiang University - University of Illinois at Urbana-Champaign Institute（浙江大学伊利诺伊大学厄巴纳香槟校区联合学院）
Class imbalance has a detrimental effect on the predictive performance of most supervised learning algorithms as the imbalanced distribution can lead to a bias preferring the majority class. To solve this problem, we propose a Supervised Contrastive Learning (SCL) method with Tree-structured Parzen Estimator (TPE) technique for imbalanced tabular datasets. Contrastive learning (CL) can extract the information hidden in data even without labels and has shown some potential for imbalanced learning tasks. SCL further considers the label information based on CL, which also addresses the insufficie…
University of Electronic Science and Technology of China（电子科技大学）；Huawei Technologies（华为技术有限公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Lab（鹏城实验室）
Learning with imbalanced data is a challenging problem in deep learning. Over-sampling is a widely used technique to re-balance the sampling distribution of training data. However, most existing over-sampling methods only use intra-class information of minority classes to augment the data but ignore the inter-class relationships with the majority ones, which is prone to overfitting, especially when the imbalance ratio is large. To address this issue, we propose a novel over-sampling model, called Majority-Guided VAE~(MGVAE), which generates new minority samples under the guidance of a majority…
Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big success, in this work, we fully customize them for generating fake tabular data. In particular, we are interested in oversampling minor classes since imbalanced classes frequently lead to sub-optimal training outcomes. To our knowledge, we are the first presenting a score-based tabular data oversampling method. Firstly, we re-design our own score …
University of Rostock（罗斯托克大学）；Leibniz-Institute for Food Systems Biology（莱布尼茨食品系统生物学研究所）；Technical University of Munich（慕尼黑工业大学）；Technische Universität Dresden（德累斯顿工业大学）
Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample gen…
Feedzai；Faculdade de Ciências da Universidade do Porto（波尔图大学理学院）；INESCTEC Universidade do Porto（波尔图大学系统与计算机工程、技术与科学研究所）
Evaluating new techniques on realistic datasets plays a crucial role in the development of ML research and its broader adoption by practitioners. In recent years, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks. However, tabular data -- which is prevalent in many high-stakes domains -- has been lagging behind. To bridge this gap, we present Bank Account Fraud (BAF), the first publicly available privacy-preserving, large-scale, realistic suite of tabular datasets. The suite was generated by applying state-of-the-art tabul…
Highly Imbalanced Regression with Tabular Data in SEP and Other Applications
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Aerospace, Physics and Space Sciences（航空航天、物理与空间科学系）；Florida Institute of Technology（佛罗里达理工学院）
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；MBZUAI（马克斯·普朗克人工智能研究所）；University of Rochester（罗切斯特大学）；MIT-IBM Watson AI Lab, IBM Research（MIT-IBM沃森人工智能实验室，IBM研究院）；Stony Brook University（石溪大学）；Brookhaven National Laboratory（布鲁赫斯巴赫国家实验室）
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets
Université Claude Bernard Lyon 1（里昂第一大学）；Aix Marseille Université（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；Centrale Marseille（马赛中央理工学院）；I2M（数学与力学研究所）；Université du Québec à Montréal（蒙特利尔大学）
Universiti Teknologi Malaysia（马来西亚理工大学）；Higher Institute for Science and Technology（高等科学技术学院）；Imam Abdulrahman Bin Faisal University（伊玛目阿卜杜拉赫曼·本·费萨尔大学）
One of the most studied machine learning challenges that recent studies have shown the susceptibility of deep neural networks to is the class imbalance problem. While concerted research efforts in this direction have been notable in recent years, findings have shown that the canonical learning objective, empirical risk minimization (ERM), is unable to achieve optimal imbalance learning in deep neural networks given its bias to the majority class. An alternative learning objective, group distributionally robust optimization (gDRO), is investigated in this study for imbalance learning, focusing …
Guaranteeing the security of transactional systems is a crucial priority of all institutions that process transactions, in order to protect their businesses against cyberattacks and fraudulent attempts. Adversarial attacks are novel techniques that, other than being proven to be effective to fool image classification models, can also be applied to tabular data. Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the Artificial Intelligence (AI) system to return incorrect outputs that are advantageous for the attacker. In this paper we…
An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care
Computer Vision Group, Institute of Cognitive Science, Osnabrück University（计算机视觉组，认知科学研究所，奥斯纳布吕克大学）；Department of Mathematics, Rhodes University（数学系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（国家理论与计算科学研究所（NITheCS））
K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data
Department of Informatics and Telecommunications, University of Ioannina（约阿尼纳大学信息与电信系）；Industrial Systems Institute, Athena Research Center（雅典娜研究中心工业系统研究所）；Archimedes Unit, Athena Research Center（雅典娜研究中心阿基米德单位）；Department of Computer Engineering and Informatics, University of Patras（帕特雷大学计算机工程与信息学系）；Department of Mechanical Engineering and Aeronautics, University of Patras（帕特雷大学机械工程与航空系）
A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens（雅典国家技术大学电气与计算机工程学院决策支持系统实验室）
Understanding the Surprising Generalization Properties of Tabular Foundation Models
Polytechnique Montréal（蒙特利尔理工学院）；Mila – Quebec AI Institute（米拉-魁北克人工智能研究所）；Chandar Research Lab（钱达尔研究实验室）；University of Toronto（多伦多大学）；Layer 6 AI（第六层人工智能公司）；Prior Labs（普里奥实验室）；ELLIS Institute Tübingen（埃利斯研究所图宾根分部）；University of Freiburg（弗赖堡大学）；Cohere（科here公司）
University of Luxembourg（卢森堡大学）；Delft University of Technology（代尔夫特理工大学）
In practice, machine learning experts are often confronted with imbalanced data. Without accounting for the imbalance, common classifiers perform poorly and standard evaluation metrics mislead the practitioners on the model's performance. A common method to treat imbalanced datasets is under- and oversampling. In this process, samples are either removed from the majority class or synthetic samples are added to the minority class. In this paper, we follow up on recent developments in deep learning. We take proposals of deep generative models, including our own, and study the ability of these ap…
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring
Risk Management Division, China Development Bank（国家开发银行风险管理部）；School of Finance, Central University of Finance and Economics（中央财经大学金融学院）；GF Holdings (Hong Kong) Corporation Limited（广发控股（香港）有限公司）；University of Cambridge（剑桥大学）
The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago. Though GAN based models seem promising, there has been a lack of purpose built architectures for solving the aforementioned problem, as most previous studies focus on applying already existing models. This paper proposes a unique, performance-oriented, data-generating strategy that utilizes a new architecture, coined draGAN, to generate both minority and majority samp…
Quantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
organization= Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California , city= Los Angeles , state= CA , postcode= 90089 , country= USA；organization= Department of Industrial Engineering；Management, Khulna University of Engineering \& Technology (KUET) , city= Khulna , postcode= 9203 , country= Bangladesh；organization= Department of Computer Science, American International University-Bangladesh (AIUB) , city= Dhaka , postcode= 1229 , country= Bangladesh
University of Sao Paulo（圣保罗大学）；University of Tsukuba（筑波大学）
In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained u…
