1.一种基于GAN‑SMOTE合成算法解决数据不平衡问题的方法,用于对机器学习所使用的训练数据集进行处理,其特征在于:包括以下步骤;步骤S1:产生随机噪声:即针对原始训练集数据,使用SMOTE算法合成一些新的少类别样本作为随机噪声;步骤S2:建立生成器,将步骤S1的随机噪声输入生成器,输出得到生成样本;步骤S3:建立判别器,输入真...
不平衡程度不同的数据用SMOTE算法为代表的4种经典过采样算法来平衡数据集,发现使用合适的过采样算法,能优化分类器对不平衡数据的分类效果;随后,将GAN用于类别不平衡问题,对类别不平衡数据中少数样本进行生成,达到平衡数据集的目的,提出一种过采样算法GAN... 刘亚明 - 湖北工业大学 被引量: 0发表: 0年 加载更多...
An innovative Credit Card Fraud Detection Algorithm that merges two potent techniques, Autoencoders and Ensemble Synthesized Minority Oversampling Techniques using GANs (ESMOTE- GAN), to effectively address the pressing issue of credit card online payment fraud. The fraud detection, traditional models ...
In this work, this problem is addressed by creating a machine learning model, which can handle the data imbalance, using CTGAN (Conditional Tabular Generative Adversarial networks) and SMOTE (Synthetic Minority Oversampling Technique) algorithms, and detect the trojan infected nets....
37.6s 35 Class Distribution After SMOTE: 37.6s 36 Is Fraudulent 37.6s 37 0 1119285 37.6s 38 1 1119285 37.6s 39 Name: count, dtype: int64 261.7s 40 261.7s 41 Random Forest - Original Data 261.7s 42 Accuracy: 0.953964627231954 261.7s 43 Confusion Matrix: 261.7s 44 [[22355 ...
Fraud Detection CSE445 - Original Vs. SMOTE & GANNotebookInputOutputLogsComments (0)Input Data Fraudulent_E-Commerce_Transaction_Data.csv(392.82 MB) get_app chevron_right Unable to show preview Unexpected token '<', "<!doctype "... is not valid JSON Input (399.12 MB) folder Data Sources ...
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