a classification method based on two separating hyper surfaces
In this paper, we propose a method that, in contrast to traditional classification methods based on Hyper Surface Classification (HSC), uses two - one inner and one outer - classification hyper-surfaces (CHS). The outer CHS includes the inner CHS together with the set that it delimits. These two CHS allow the authors to define a new classification rule that uses the smallest distance between the point to be classified and the intersection points of the double CHS with a ray starting at this point. In contrast to known classification rules used in HSC-methods, this innovative rule is reliable and easy to implement in practice, which makes the proposed classification method accurate and efficient.
classification rules multi-dimensional classification hyper-surface classification methods pattern recognition compact sets of points