Particle Filter Stochastic Resampling Algorithm

In machine learning, stochastic resampling is a commonly used technique that helps address data imbalance issues effectively. This method creates new datasets by randomly selecting samples from the original data, thereby increasing the overall sample size while balancing the distribution across different classes. Common stochastic resampling approaches include undersampling (reducing majority class samples) and oversampling (increasing minority class samples), where the choice depends on specific application requirements. From an implementation perspective, resampling algorithms typically utilize random number generators and probability distributions to determine sample selection. For example, in Python's scikit-learn library, the resample() function from sklearn.utils can be employed with parameters specifying sampling strategy and random state for reproducibility. The core algorithm often involves calculating class weights or using techniques like SMOTE (Synthetic Minority Over-sampling Technique) for intelligent oversampling, which generates synthetic samples rather than simple duplication.