A maximum margin classifier for non-linearly separable pattern classes, using a feature space sampling technique, applied to chromosome classification
by S. Ganesh Vaidyanathan, Bibhas Kar, N. Kumaravel
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 4, No. 2, 2010

Abstract: 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.

Online publication date: Sat, 07-Aug-2010

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