Summary
In this chapter, we have used a dataset having more than 284,807 instances of credit card use and for each transaction where only 0.172% transactions are fraudulent. We have seen how we can use autoencoders to pre-train a classification model and how to apply anomaly detection techniques to predict possible fraudulent transactions from highly imbalanced data—that is, we expected our fraudulent cases to be anomalies within the whole dataset.
Our final model now correctly identified 83% of fraudulent cases and almost 100% of non-fraudulent cases. Nevertheless, we have seen how to use anomaly detection using outliers, some ways of hyperparameter tuning, and, most importantly, feature selection.
A recurrent neural network (RNN) is a class of artificial neural network where connections between units form a directed cycle. RNNs make use of information from the past. That way, they can make predictions in data with high temporal dependencies. This creates an internal state of the network...
