Machine and Deep Learning Solutions for Imbalanced Data

Choosing the right computational approach for imbalanced data classification can significantly affect the accuracy and efficacy of predictive models. In this lesson, we explore the strengths and limitations of machine learning and deep learning in this context, aiming to provide insights that will guide our methodological choices in tackling rare event prediction problems.

Machine Learning (ML) is known for its simplicity especially in regards to its interpretability. Machine learning methods are, therefore, usually the first choice for most problems. Deep learning, on the other hand, provides more possibilities.

As we discussed earlier, we might have an imbalanced binary labeled multivariate time series process. For over two decades, imbalanced binary classification has been actively researched. However, there are still several open challenges in this topic.

Machine learning

One major open challenge for machine learning methods is the absence of a robust and simple modeling framework. The summarization of ML methods in the illustration below makes this more evident.