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How does a SVM handle an imbalanced class?
Essentially this is equivalent to oversampling the minority class: for instance if C p o s = 2 C n e g this is entirely equivalent to training a standard SVM with C = C n e g after including every positive twice in the training set. SVMs are able to deal with datasets with imbalanced class frequencies.
When was the misclassification penalty introduced in SVM?
The misclassification penalty for the minority class is chosen to be larger than that of the majority class. This approach was introduced quite early, it is mentioned for instance in a 1997 paper: Edgar Osuna, Robert Freund, and Federico Girosi. Support Vector Machines: Training and Applications.
How does class imbalance affect machine learning classification?
So when we have a class imbalance, the machine learning classifier tends to be more biased towards the majority class, causing bad classification of the minority class. The Accuracy Paradox refers to the utility of using the Accuracy out of our Confusion Matrix as a metric for predictive modelling when classifying imbalanced classes.
Can a SVM be used for sparse data?
In the case of sparse data like that SVM will work well. As stated by @Bitwise you should not use accuracy to measure the performance of the algorithm. Instead you should calculate the precision, recall and F-Score of the algorithm. Thanks for contributing an answer to Cross Validated!
How are weighted support vector machines used for imbalanced classification?
This modification of SVM that weighs the margin proportional to the class importance is often referred to as weighted SVM, or cost-sensitive SVM. In this tutorial, you will discover weighted support vector machines for imbalanced classification.
When to use larger or smaller weighting in SVM?
A larger weighting can be used for the minority class, allowing the margin to be softer, whereas a smaller weighting can be used for the majority class, forcing the margin to be harder and preventing misclassified examples. Small Weight: Smaller C value, larger penalty for misclassified examples.
Which is the correct margin for imbalanced classification?
By default, this margin favors the majority class on imbalanced datasets, although it can be updated to take the importance of each class into account and dramatically improve the performance of the algorithm on datasets with skewed class distributions.