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Class imbalance is a common challenge in deep learning where some classes have significantly fewer examples than others. This imbalance can lead to biased models that perform poorly on minority classes. Addressing this issue involves techniques such as data sampling and cost-sensitive learning.
Data Sampling Techniques
Data sampling adjusts the distribution of training data to balance class representation. Common methods include oversampling minority classes and undersampling majority classes. These techniques help the model learn equally from all classes.
Cost-sensitive Learning
Cost-sensitive learning assigns higher misclassification costs to minority classes. This approach encourages the model to pay more attention to underrepresented classes during training, improving overall performance on imbalanced datasets.
Implementation Strategies
Effective strategies include combining data sampling with cost-sensitive loss functions. Additionally, techniques like focal loss can focus training on hard-to-classify examples, further mitigating class imbalance issues.
- Oversampling minority classes
- Undersampling majority classes
- Applying class weights in loss functions
- Using focal loss