How to deal with imbalanced dataset

32) What is an imbalanced dataset? Can you list some ways to deal with it? [src]

An imbalanced dataset is one that has different proportions of target categories. For example, a dataset with medical images where we have to detect some illness will typically have many more negative samples than positive samples—say, 98% of images are without the illness and 2% of images are with the illness.

There are different options to deal with imbalanced datasets:

• Oversampling or undersampling. Instead of sampling with a uniform distribution from the training dataset, we can use other distributions so the model sees a more balanced dataset.

• Data augmentation. We can add data in the less frequent categories by modifying existing data in a controlled way. In the example dataset, we could flip the images with illnesses, or add noise to copies of the images in such a way that the illness remains visible.

• Using appropriate metrics that describe the accuracy of the model better when using an imbalanced dataset.
  • In the example dataset, if we had a model that always made negative predictions, it would achieve a precision of 98%.
  • There are other metrics such as
    • precision, recall
    • a combination of the two in an F-beta score which allows us to prioritize one over the other
    • F-score

• Collect more data to even the imbalances in the dataset.

• Resample the dataset to correct for imbalances.

• Try a different algorithm altogether on your dataset.

Dealing with imbalanced datasets is a common challenge in machine learning, especially in classification problems where the number of instances across different classes significantly varies. This imbalance can lead to poor model performance, particularly for the minority class, as the model might get biased towards the majority class. Here are several strategies to address this issue:

1. Resampling Techniques

• Oversampling Minority Class: Increase the number of instances in the minority class by replicating them to balance the dataset. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) generate synthetic samples rather than simply duplicating existing ones.

• Undersampling Majority Class: Reduce the number of instances in the majority class to match the minority class size. This can lead to a loss of potentially valuable data, so it should be used cautiously.

2. Modify Class Weights

Adjust the weights of different classes in the loss function so that the model pays more attention to the minority class. Many machine learning algorithms have a parameter that allows setting class weights inversely proportional to class frequencies or based on custom values that can be fine-tuned.

3. Anomaly Detection Techniques

In extreme cases of imbalance, treating the problem as an anomaly detection (or outlier detection) task rather than a classification task can be more effective. Anomaly detection algorithms are designed to identify rare events or observations, which can be analogous to detecting instances of the minority class.

4. Cost-sensitive Learning

Modify the learning algorithm to make the cost of misclassifying minority class instances higher than that of misclassifying majority class instances. This approach encourages the model to focus more on correctly classifying minority class instances.

5. Ensemble Methods

• Bagging: Use ensemble methods like Bagging (Bootstrap Aggregating) to reduce variance and improve model stability. Bagging can be particularly effective when combined with undersampling.

• Boosting: Algorithms like AdaBoost can focus more on difficult to classify instances, often those belonging to the minority class, by iteratively reweighting the training data.

• Balanced Random Forests: A variation of the Random Forest algorithm that builds multiple trees on different subsamples of the data, ensuring that each tree is trained on a balanced subset.

6. Data Augmentation

For certain types of data, such as images or text, generating new instances through data augmentation techniques can help balance the dataset. This includes transformations like rotation, flipping, or cropping for images, and synonym replacement or sentence rephrasing for text.

7. Advanced Algorithms

Explore advanced algorithms and techniques specifically designed for imbalanced datasets. These include methods like cost-sensitive neural networks, minority oversampling in feature space (MOFS), and ensemble of subset sampling techniques.

8. Evaluation Metrics

Use appropriate evaluation metrics that are not biased towards the majority class. Accuracy can be misleading in imbalanced datasets. Metrics like Precision, Recall, F1-score, the area under the Receiver Operating Characteristic (ROC) curve (AUC-ROC), and the area under the Precision-Recall curve (AUC-PR) are more informative.

Implementation Tips

• Experiment with multiple approaches: No single technique is universally best for all imbalanced datasets. It often helps to try multiple approaches and combinations thereof to find what works best for your specific problem.

• Cross-validation: Use stratified cross-validation to ensure that each fold is representative of the overall class distribution, helping to maintain the imbalance proportion across training and validation sets.

Addressing dataset imbalance requires careful consideration of the problem context, the available data, and the desired outcome. It's often a process of experimentation and fine-tuning to find the most effective solution.