Shell fitting space for classification

In this paper, a shell fitting space (SFS) is presented to map non-linearly separable data to linearly separable ones. A linear or quadratic transformation maps data into a new space for better classification, if the transformation method is properly guessed. This new SFS space can be of high or low dimensionality, and the number of dimensions is generally low and it is equal to the number of classes. The SFS method is based on fitting a hyper-plane or shell to the learning data or enclosing them into a hyper-surface. In the proposed method, the hyper-planes, curves, or cortex become the axis of the new space. In the new space a linear support vector machine (SVM) multi-class classifier is applied to classify the learn data.