Imbalanced Learning - The Complete Guide
Description
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 imbalanced data poses major challenges and is recognized as needing significant attention.
The problem with imbalanced 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 this problem.
- Go over the major state-of-the-art methods and techniques that you can use to deal with this problem.
- Explain the advantages and drawback of different approaches .
- Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.
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