Categories
28°C, New York
Home Machine Learning Use Cases of One-Class SVM

Use Cases of One-Class SVM

One-Class SVM in Anomaly Detection

One-Class SVM Kernel Trick

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

  1. Detecting fraud in financial transactions: OCSVM excels in rare cases that are not uncommon associated with fraudulent activity in financial transactions. With specialized training in general practices, it becomes adept at distinguishing specific patterns. During testing, deviations from these scholarly models are immediately flagged, indicating that the fraud can be treated as abnormalities.
  2. Fault detection in commercial systems: Companies that rely on complex devices can benefit from OCSVM’s real-time monitoring of defects or anomalies. When applied to sensor data, OCSVM identifies abnormal behavior, and identifies potential errors. Early detection through OCSVM prevents maintenance, reduces downtime and increases operational efficiency.
  3. Network Intrusion Detection: OCSVM can play an important role in continuously monitoring computer networks to protect against malicious activity. It helps identify unusual network behaviors that may indicate a possible attack. OCSVM works well in situations where most network traffic is normal and anomalies are very rare.
  4. Quality Control in Manufacturing: Strict quality control is needed in manufacturing to ensure fault-free products. OCSVM is applied on sensor data or product characteristics to detect deviations from the perfect product. It helps to detect defects early during production.

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.

Similar Reads

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...

Tags:
#Machine Learning #AI-ML-DS #Machine Learning #Machine Learning #Machine Learning

One-Class SVM in Anomaly Detection

One-Class SVM Kernel Trick