Grid-Based Fuzzy Support Vector Data Description

Support Vector Data Description (SVDD) concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a data set can be used to detect outliers. SVDD is affected by noises during being trained. In this paper, Grid-based Fuzzy Support Vector Data Description (G-FSVDD) is presented to deal with the problem. G-FSVDD reduces the effects of noises by a new fuzzy membership model, which is based on grids. Each grid is a hypercube in data set. After obtaining enough grids, Apriori algorithm is used to find grids with high density. In G-FSVDD, different training data make different contributions to the domain description according to their density. The advantage of G-FSVDD is shown in the experiment.