A maximum margin classifier for non-linearly separable pattern classes, using a feature space sampling technique, applied to chromosome classification. (9th August 2010)

• Record Type: Journal Article
• Authors: Ganesh Vaidyanathan, S.; Kar, Bibhas; Kumaravel, N.
• Is Part Of: International journal of biomedical engineering and technology. Volume 4:Number 2(2010)
• Page Start: 123
• Page End: 133
• Publication Date: 2010-08-09
• Subjects: feature space -- curve fitting -- sampling -- optimal boundary points -- nonlinear boundaries -- binary pattern classifiers -- chromosomes classification; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

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.