How One-Class SVM Works?
One-Class Support Vector Machines (OCSVM) operate on a fascinating principle inspired by the idea of isolating the norm from the abnormal in a dataset. Unlike traditional Support Vector Machines (SVM), which are adept at handling binary and multiclass classification problems, OCSVM specializes in the nuanced task of anomaly detection. The workflow of OCSVM is discussed below:
- Conceptual Foundation: OCSVM establishes itself on the premise that the majority of real-world data is inherently normal. In most scenarios, outliers or anomalies are rare occurrences that deviate significantly from the usual patterns. OCSVM’s goal is to define a boundary encapsulating the normal instances in the feature space, thereby creating a region of familiarity.
- Outlier Boundary Definition: The algorithm crafts a boundary around the normal instances, often referred to as the “normalcy region.” This boundary is strategically positioned to maximize the margin around the normal data points, allowing for a clear delineation between what is considered ordinary and what may be deemed unusual. Think of it as drawing a protective circle around the typical instances to shield them from the outliers or anomalies.
- Margin Maximization: The heart of OCSVM lies in its commitment to maximizing the margin between the normal instances and the boundary. A larger margin provides a robust separation, enhancing the model’s ability to discern anomalies during testing. This emphasis on margin maximization is akin to creating a safety buffer around the normal instances, fortifying the model against the influence of potential outliers or anomalies.
- Training Process: During the training phase, OCSVM exclusively leverages the majority class or normal instances. This unimodal focus distinguishes it from traditional SVMs, which necessitate examples from both classes for effective training. By concentrating solely on the norm, OCSVM tailors itself to scenarios where anomalies are sparse, and labeled instances of anomalies are hard to come by. It comes with a fantastic hyperparameter called ‘nu’. This parameter acts as an upper bound on the fraction of margin errors and support vectors allowed by the model. Tuning the nu parameter enables practitioners to strike a balance between the desire for a stringent model that minimizes false positives (normal instances misclassified as anomalies) and a more lenient model that embraces a higher fraction of anomalies.
- Testing and Anomaly Identification: Armed with the learned normalcy region, OCSVM can swiftly identify anomalies during testing. Instances falling outside the defined boundary are flagged as potential outliers. The model essentially acts as a vigilant guardian, scrutinizing new data points and signaling if they exhibit behavior significantly different from the norm.
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.