How does One-Class SVM differ from SVM?
SVM and one-class SVMs are like twins but not identical twins, as their usage and principals are different. The three most common differences are discussed below:
Aspects |
Support Vector Machine |
One-Class Support Vector Machine |
---|---|---|
Single-Class Training |
Requires labeled data from both classes for training |
Operates with only the majority class during training |
Imbalance Handling |
Can’t handle imbalance nature of datasets. |
Inherently addresses class imbalance, prevalent in outlier detection tasks. By concentrating on the majority class during training. |
Outlier Detection Focus |
Only aims to find a hyperplane that best separates multiple classes. |
Excels in scenarios where the goal is to uncover instances that deviate from the norm like in fraud detection or fault monitoring. |
Understanding One-Class Support Vector Machines
Support Vector Machine is a popular supervised machine learning algorithm. it is used for both classifications and regression. In this article, we will discuss One-Class Support Vector Machines model.