A Deep Learning-Based Alternate Iteration Algorithm for One-Class Classification

Music City Center 

One-class classification (OCC) is a specialized machine learning approach designed for scenarios in which only data from a single class (target class) and any other points are considered outliers. Support Vector Data Description (SVDD) effectively finds a hypersphere enclosing the target class in OCC. In this research, we establish a new method that integrates a deep neural network with Least Squares Support Vector Data Description(LS-SVDD) to perform one-class classification by learning a feature space that encloses the target data within a minimal hypersphere. The parameters are optimized using an alternating iterative algorithm, ensuring both high accuracy and fast convergence. With the network weights fixed, the neural network's output serves as input to the LS-SVDD, where the center and radius are determined. The neural network parameters are then updated through backpropagation. This approach allows us to refine the model iteratively, leading to more precise parameter estimation and enhanced anomaly. To evaluate the performance, several real-world publicly available datasets were used.

One Class Classification

LS-SVDD

Deep Learning 

Section on Statistical Learning and Data Science