One-Class Classification

What is One-class classification? One-class classification Explained

One-class classification, also known as one-class learning or anomaly detection, is a machine learning task where the goal is to classify instances into one specific class, often referred to as the target class or the positive class. Unlike traditional classification, where multiple classes are considered, one-class classification focuses on distinguishing instances belonging to a specific class from all other instances that do not belong to that class.

The main objective of one-class classification is to learn a model that captures the characteristics of the target class and can identify novel or anomalous instances that deviate significantly from those characteristics. It is commonly used when the negative class (instances not belonging to the target class) is not well-defined or difficult to obtain sufficient labeled data for training.

There are several approaches to performing one-class classification:

Density-based methods: These methods estimate the density of the target class and consider instances falling in low-density regions as anomalies. Examples include kernel density estimation, Gaussian mixture models, and local outlier factor (LOF).

Distance-based methods: These methods define a distance metric or similarity measure between instances and determine anomalies based on their distances to the target class. For instance, the k-nearest neighbor (k-NN) algorithm can be used to measure the distance between instances and identify outliers.

Reconstruction-based methods: These methods learn a model to reconstruct the instances of the target class accurately. Instances that cannot be effectively reconstructed are considered anomalies. Autoencoders, which are neural networks trained to reconstruct input data, are commonly used for reconstruction-based one-class classification.

Boundary-based methods: These methods aim to learn a boundary that encapsulates the target class instances. Instances lying outside this boundary are classified as anomalies. Support vector machines (SVMs) with a radial basis function (RBF) kernel and one-class SVMs fall under this category.

Ensemble-based methods: These methods combine multiple one-class classifiers to improve the overall performance and robustness. Ensemble techniques like bagging, boosting, or combination rules can be applied to one-class classifiers.

One-class classification finds applications in various domains such as fraud detection, network intrusion detection, computer security, outlier detection in data analysis, and quality control in manufacturing, among others. It allows the detection of abnormal instances without requiring extensive labeled data for the negative class, making it particularly useful in scenarios where anomalies are rare or difficult to define explicitly.