This fashion, extra information is available to make predictions. Now that the forecasting models are chosen for every macroeconomic indicator, and the predictions are made, we use the forecast values as enter to the skilled fashions in step 3. Gradient boosting machine was selected as the best performing machine learning mannequin using feature choice with optimum lags, and ridge regression was the winner when using feature choice with all lags. This selection was performed based on if the analysis paper is pursuing a novel, attention-grabbing, or relevant method which will be extended to the case of credit card fraud detection. Finally, this experiment discerns the feasibility of bettering fraud detection by way of a multi-model voting system. The authors in (Kim and Kim, 2002) suggest an aggregated framework based mostly on neural networks and a fraud density map. The latest work of the applying of GANs on creating artificial samples to stability the class in classification issues is Douzas and Bacao (2018), cGAN performance was evaluated on 71 datasets with different imbalance ratios, variety of options and subclustering structures and in comparison with multiple oversampling strategies, using Logistic Regression, Support Vector Machine, Nearest Neighbors, Resolution Timber and Gradient Boosting Machine as classifiers. Capital One has blogged about their inside work using GANs to generate synthetic transaction data (?).

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