Self-adaptive SVDD integrated with AP clustering for one-class classification

Abstract

The Support Vector Data Description (SVDD) is one of the best-known one-class-classification methods used to solve problems where the sample data are of high dimension but limited amount. The results of SVDD can be greatly affected when the target data are poorly distributed and their density varies extremely. To deal with situations like these, we propose an improved SVDD algorithm, termed as Self-Adaptive SVDD (SA_SVDD), which combines Affinity Propagation (AP) clustering algorithm and SVDD. Firstly, SA_SVDD applies AP clustering to the input data set to obtain a set of compact subclasses, and then the boundary for each subclass is identified by SVDD, and these boundaries are exploited as final classification criteria. AP clustering is tried with different input preferences generated with Latin Hypercube Sampling, and an improved Particle Swarm Optimization (PSO) algorithm evolves the parameters of SVDD. The proposed algorithm is evaluated on a conventional benchmark set and shows significant improvement with respect to other one-class-classification methods.