What Upsampling Meaning, Applications & Example
The process of increasing the resolution or size of data.
What is Upsampling?
Upsampling is a technique used to increase the number of instances in the minority class in an imbalanced dataset. This is typically done by duplicating existing data points or generating synthetic data . The goal of upsampling is to create a more balanced dataset that can help improve the performance of machine learning models, especially in classification tasks where one class is underrepresented.
Methods of Upsampling
- Random Upsampling: Randomly duplicates instances from the minority class to increase its representation.
- SMOTE (Synthetic Minority Over-sampling Technique): Generates synthetic samples by interpolating between existing instances of the minority class.
- ADASYN (Adaptive Synthetic Sampling): Similar to SMOTE, but it focuses more on generating synthetic data for difficult-to-classify instances.
Applications of Upsampling
- Imbalanced Classification Problems: In tasks like fraud detection or disease prediction, where the number of positive instances is much smaller than negative instances.
- Time-Series Forecasting: In situations where there is an imbalance in the occurrence of events over time, upsampling can be used to create a more even distribution of event types.
- Text Classification: When certain categories of text data are underrepresented, upsampling can help balance the dataset for better model training .
Example of Upsampling
In a spam email classifier, if the dataset contains 90% non-spam emails and only 10% spam emails, upsampling can be applied to increase the number of spam emails in the dataset, either by duplicating existing spam samples or generating synthetic samples. This helps the model learn to better identify spam, as it will have more balanced exposure to both classes during training.