Undersampling techniques are used to address class imbalance in data. There are two main categories of undersampling: controlled and uncontrolled. Controlled techniques involve selecting a specific number of samples, while uncontrolled techniques remove points that meet certain conditions. Some examples of controlled and uncontrolled undersampling methods include random undersampling, k-means undersampling, Tomek Links undersampling, and Edited Nearest Neighbors undersampling. These techniques help to preserve the distribution of data and improve the balance between classes.
Class Imbalance: Exploring Undersampling Techniques
In this article, we will discuss the concept of class imbalance and how it can be addressed using undersampling techniques. Class imbalance occurs when the number of samples in different classes of a dataset is significantly different, which can lead to biased machine learning models. We will explore both controlled and uncontrolled undersampling techniques and their practical applications.
Naive Random Undersampling
Naive random undersampling is a controlled technique where a specified number of samples are randomly chosen and removed from the majority class. This helps to balance the class distribution in the dataset. However, this method may alter the overall data distribution.
K-Means Undersampling
K-means undersampling is another controlled technique that aims to preserve the distribution of the data. It involves performing K-means clustering with the desired number of samples for each class. The resulting centroids or their nearest neighbors are selected as the final samples. This method ensures a smaller set of points that still represent the original data distribution.
Tomek Links Undersampling
Tomek links undersampling is an uncontrolled technique that removes samples based on the presence of Tomek links. Tomek links are pairs of samples from different classes that are each other’s nearest neighbors. Removing these samples helps to improve the decision boundary and balance the data distribution.
Edited Nearest Neighbors Undersampling
Edited nearest neighbors undersampling is another uncontrolled technique that removes samples based on their neighbors. It keeps a sample if the majority of its neighbors belong to the same class. This method helps to eliminate noisy points that may not contribute to the decision boundary.
By applying these undersampling techniques, you can address class imbalance in your datasets and improve the performance of your machine learning models. It is important to carefully choose the appropriate technique based on your specific needs and data characteristics.
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