Chapter 5: Autoencoder for Fraud Detection

At this point in the book, you should already know the basic math and concepts behind neural networks and some deep learning paradigms, as well as the most useful KNIME nodes for data preparation, how to build a neural network, how to train it and test it, and finally, how to evaluate it. We have built together, in Chapter 4, Building and Training a Feedforward Neural Network, two examples of fully connected feedforward neural networks: one to solve a multiclass classification problem on the Iris dataset and one to solve a binary classification problem on the Adult dataset.

Those were two simple examples using quite small datasets, in which all the classes were adequately represented, with just a few hidden layers in the network and a straightforward encoding of the output classes. However, they served their purpose: to teach you how to assemble, train, and apply a neural network in KNIME Analytics Platform.

Now, the time has come to...