Imbalanced Learning (Unbalanced Data) - The Complete Guide

Learn how to handle imbalanced data in Machine Learning. Data based approaches, algorithmic approaches and more!

This is a niche topic for students interested in data science and machine learning fields. The classical data imbalance problem is recognized as one of the major problems in the field of data mining and machine learning. Imbalanced learning focuses on how an intelligent system can learn when it is provided with unbalanced data.

There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from unbalanced data poses major challenges and is recognized as needing significant attention.

The problem with unbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions.

The specific goals of this course are:

• Help the students understand the underline causes of unbalanced data problem.

• Go over the major state-of-the-art methods and techniques that you can use to deal with imbalanced learning.

• Explain the advantages and drawback of different approaches and methods .

• Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.