What is undersampling in machine learning?
Undersampling is a technique to balance uneven datasets by keeping all of the data in the minority class and decreasing the size of the majority class. It is one of several techniques data scientists can use to extract more accurate information from originally imbalanced datasets.
What do we do in undersampling?
In signal processing, undersampling or bandpass sampling is a technique where one samples a bandpass-filtered signal at a sample rate below its Nyquist rate (twice the upper cutoff frequency), but is still able to reconstruct the signal.
Which is better to use undersampling or oversampling?
Typically, undersampling methods are used in conjunction with an oversampling technique for the minority class, and this combination often results in better performance than using oversampling or undersampling alone on the training dataset.
How does undersampling work for imbalanced data sets?
… undersampling, that consists of reducing the data by eliminating examples belonging to the majority class with the objective of equalizing the number of examples of each class … — Page 82, Learning from Imbalanced Data Sets, 2018.
What does near miss mean in undersampling algorithms?
Near Miss refers to a collection of undersampling methods that select examples based on the distance of majority class examples to minority class examples. The approaches were proposed by Jianping Zhang and Inderjeet Mani in their 2003 paper titled “ KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction .”
Which is the most common technique for over sampling?
The most common technique is known as SMOTE: Synthetic Minority Over-sampling Technique. To illustrate how this technique works consider some training data which has s samples, and f features in the feature space of the data. Note that these features, for simplicity, are continuous. As an example, consider a dataset of birds for classification.