One-Class Support Vector Machines
One-Class Support Vector Machine is a special variant of Support Vector Machine that is primarily designed for outlier, anomaly, or novelty detection. The objective behind using one-class SVM is to identify instances that deviate significantly from the norm. Unlike other traditional Machine Learning models, one-class SVM is not used to perform binary or multiclass classification tasks but to detect outliers or novelties within the dataset. Some of the key working principles of one-class SVM is discussed below.
- Outlier Boundary: One-Class SVM operates by defining a boundary around the majority class (normal instances) in the feature space. This boundary is constructed to encapsulate the normal data points, effectively creating a region of normalcy.
- Margin Maximization: This algorithm strives to maximize the margin around the normal instances, allowing for a more robust separation between normal and anomalous data points. This margin is crucial for accurately identifying outliers during testing.
- High sensitivity: One-Class SVM has an in-build hyperparameter called “nu,” representing an upper bound on the fraction of margin errors and support vectors. Fine-tuning this parameter influences the model’s sensitivity to outliers.
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.