A maximum margin classifier for non-linearly separable pattern classes, using a feature space sampling technique, applied to chromosome classification
Ganesh Vaidyanathan, Bibhas Kar, N. Kumaravel · International Journal of Biomedical Engineering and Technology · 2010
The classification of chromosomes using a classifier is generally inaccurate owing to closeness of features belonging to various chromosomes which poses a linearly inseparable problem. This paper proposes a novel technique to obtain the non-linear decision boundary. An average classification accuracy of 93% was achieved with this technique which involves arriving at the non-linear decision boundary by joining and smoothening the sample points obtained by sampling the feature space within a boundary limited by the range of the data and by the curves of the best fit to the two classes. The technique works for feature space of any dimension.