英文标题：
《Improving Detection of Credit Card Fraudulent Transactions using
  Generative Adversarial Networks》
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作者：
Hung Ba
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最新提交年份：
2019
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英文摘要：
  In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions, we are able to improve the discriminatory power of classifiers. The experiment results show that the Wasserstein-GAN is more stable in training and produce more realistic fraudulent transactions than the other GANs. On the other hand, the conditional version of GANs in which labels are set by k-means clustering does not necessarily improve the non-conditional versions of GANs.
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中文摘要：
在这项研究中，我们采用生成性对抗网络作为过采样方法来生成人工数据，以帮助对信用卡欺诈交易进行分类。GANs是一个基于博弈论思想的生成模型，其中生成器G和鉴别器D试图智胜对方。生成器的目的是混淆鉴别器。鉴别器的目标是区分来自生成器的实例和来自原始数据集的实例。通过对机构进行一系列信用卡欺诈交易的培训，我们能够提高分类器的识别能力。实验结果表明，与其他机构相比，Wasserstein机构在训练中更稳定，产生更真实的欺诈交易。另一方面，通过k-means聚类设置标签的条件版本的GANs不一定会改进非条件版本的GANs。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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