The original contour preserving classification technique was
proposed to improve the robustness and weight fault tolerance of a neu-
ral network applied with a two-class linearly separable problem. It was
recently found to be improving the level of accuracy of two-class classi-
fication. This paper presents an augmentation of the original technique
to improve the level of accuracy of multi-class classification by better
preservation of the shape or distribution model of a multi-class problem.
The test results on six real world multi-class datasets from UCI ma-
chine learning repository present that the proposed technique supports
multi-class data and can improve the level of accuracy of multi-class
classification more effectively.