General rules to follow

Usually, the first step in any machine learning pipeline should be to split the data into train/test/validation sets. We should avoid applying any techniques to handle the imbalance until after the data has been split. We should begin by splitting the data into training, testing, and validation sets and then proceed with any necessary adjustments to the training data. Applying techniques such as oversampling (see Chapter 2, Oversampling Methods) before splitting the data can result in data leakage, overfitting, and over-optimism [6].

We should ensure that the validation data closely resembles the test data. Both validation data and test data should represent real-world scenarios on which the model will be used for prediction. Avoid applying any sampling techniques or modifications to the validation set. The only requirement is to include a sufficient number of samples from all classes.

Let’s switch to discussing a bit about using unsupervised learning algorithms. Anomaly detection or outlier detection is a class of problems that can be used for dealing with imbalanced data problems. Anomalies or outliers are data points that deviate significantly from the rest of the data. These anomalies often correspond to the minority class in an imbalanced dataset, making unsupervised methods potentially useful.

The term that’s often used for these kinds of problems is one-class classification. This technique is particularly beneficial when the positive (minority) cases are sparse or when gathering them before the training is not feasible. The model is trained exclusively on what is considered the “normal” or majority class. It then classifies new instances as “normal” or “anomalous,” effectively identifying what could be the minority class. This can be especially useful for binary imbalanced classification problems, where the majority class is deemed “normal,” and the minority class is considered an anomaly.

However, it does have a drawback: outliers or positive cases during training are discarded [7], which could lead to the potential loss of valuable information.

In summary, while unsupervised methods such as one-class classification offer an alternative for managing class imbalance, our discussion in this book will remain centered on supervised learning algorithms. Nevertheless, we recommend that you explore and experiment with such solutions when you find them appropriate.