OCSVM

The One-Class Support Vector Machine, often abbreviated as OCSVM, is a type of unsupervised machine learning algorithm used for anomaly detection. Unlike traditional classification algorithms that learn to distinguish between multiple classes, OCSVM learns a boundary around the "normal" data points. Its primary objective is to identify outliers or anomalies that do not conform to this learned boundary.

The core idea behind OCSVM is to find a hypersphere or hyperplane in a high-dimensional feature space that encloses most of the training data. When presented with new data, the algorithm checks if it falls within this learned boundary. Data points that lie outside this boundary are considered anomalies. The algorithm achieves this by mapping the data into a high-dimensional space using a kernel function, where it becomes easier to find a separating hyperplane.

OCSVM is particularly useful when dealing with datasets where anomalies are rare, or when defining what constitutes an anomaly is difficult, but examples of normal data are readily available. It does not require labeled data for anomalies, making it a valuable tool for scenarios like fraud detection, intrusion detection in networks, or identifying defective products in manufacturing. The performance of OCSVM can be influenced by the choice of kernel function and its associated parameters, such as the kernel coefficient and the nu parameter, which controls the trade-off between the number of training errors and the number of support vectors.