Undersampling is a technique used in imbalanced machine learning datasets to address the problem of class imbalance. Class imbalance occurs when one class has significantly fewer samples than the other(s), leading to biased model performance and predictions. Undersampling aims to reduce the number of samples from the majority class to balance the class distribution.
In this technique, samples from the majority class are randomly selected and removed from the dataset until a desired balance between the classes is achieved. The removed samples are discarded, and the remaining dataset is used for training the model.
There are different strategies for performing undersampling:
Random Undersampling: It involves randomly selecting samples from the majority class without any specific criteria. It is a simple and straightforward method, but it may discard useful information and potentially result in the loss of important minority class samples.
Cluster-based Undersampling: This technique involves clustering the majority class samples and then selecting representative samples from each cluster. This technique aims to preserve the diversity of the majority class while reducing its size.
Near Miss Undersampling: It selects samples from the majority class that are close to the minority class samples based on some distance metric. It ensures that the selected majority class samples are similar to the minority class samples in feature space.
Tomek Links: Tomek Links are pairs of samples from different classes that are nearest neighbors of each other. Undersampling using Tomek Links involves removing the majority class samples from these pairs, as they are considered borderline samples and may cause misclassification.
Edited Nearest Neighbors (ENN): ENN is an iterative undersampling technique that removes samples from the majority class if their class label conflicts with the labels of their nearest neighbors. This process is repeated until no more samples are removed.
Undersampling can help in mitigating the issues caused by class imbalance by providing a more balanced training dataset. However, it is important to consider the potential drawbacks of undersampling. By reducing the number of samples from the majority class, undersampling can discard potentially useful information and lead to loss of overall data representation. Undersampling may be more effective when the majority class has a large number of redundant or noisy samples.
It is also worth noting that undersampling is just one approach to handle class imbalance, and other techniques such as oversampling, class weighting, or generating synthetic samples may also be considered. The choice of the appropriate technique depends on the specific problem, dataset characteristics, and the performance requirements of the machine learning task.
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