One-Class SVM in Anomaly Detection

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....

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:...

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:...

One-Class SVM in Anomaly Detection

In the domain of anomaly detection, One-Class Support Vector Machines (OCSVM) serve as a robust and versatile tool designed to discern normal patterns from irregular occurrences. Notably, OCSVM takes a distinctive approach by training exclusively on the majority class, which represents normal instances, eliminating the need for labeled anomalous data during training. This is particularly advantageous in real-world scenarios where anomalies are rare, making it challenging to obtain sufficient labeled samples. The core principle of OCSVM involves defining a boundary around the normal instances in the feature space, achieved through a specified kernel function and a nuanced parameter termed “nu.” This parameter acts as an upper limit on the fraction of margin errors and support vectors, enabling users to fine-tune the model’s sensitivity to outliers. During the testing phase, instances falling outside this learned boundary are flagged as potential outliers, facilitating efficient anomaly identification. OCSVM’s adaptability extends to various applications, including fraud detection in financial transactions, fault monitoring in industrial systems, and network intrusion detection. Its innate ability to capture complex, non-linear relationships and its focus on the majority class make it a valuable asset in safeguarding systems against unexpected events and ensuring robust anomaly detection across diverse domains. In this article, we will implement credit card anomaly detect using OCSVM further....

Use Cases of One-Class SVM

There are several real-world use-cases of One-Class SVM which are listed below–>...

One-Class SVM Kernel Trick

One-Class SVM supports various kernel options like SVM for optimized performance which are discussed below:...

Step-by-Step implementation of One-Class Support Vector Machines in Python

Importing required modules...