This paper presents a novel Boundary-based approach in one-class classification that is inspired by support vector data description (SVDD). The SVDD is a popular kernel method which tries to fit a hypersphere around the target objects and of course more precise boundary is relied on selecting proper parameters for the kernel functions. Even with a flexible Gaussian kernel function, the SVDD could sometimes generate a loose decision boundary. Here we modify the structure of the SVDD by using a hyperellipse to specify the boundary of the target objects with more precision, in the input space. Due to the usage of a hyperellipse instead of a hypersphere as the decision boundary, we named it "Ellipse Support Vector Data Description" (ESVDD). We show that the ESVDD can generate a tighter data description in the kernel space as well as the input space. Furthermore the proposed algorithm boundaries on the contrary of SVDD boundaries are less influenced by change of the user defined parameters.