Classification of Gene Samples Using Pair-Wise Support Vector Machines

Engin Taş

Alphanumeric Journal, 2017, vol. 5, issue 2, 283-292

Abstract: The main problem in the classification problems encountered with gene samples is that the dimension of the data is high although the sample size is small. In such problems, the classifier to be used must be a classifier that allows the processing of high dimensional data and extracts maximum information from a small number of samples at hand. In this context, a classification methodology has been developed, which first transforms the problem of binary or multiple classification into separate pair-wise classification problems. To this end, an online classifier has been adapted to solve pair-wise binary classification problems. The resulting classifier performed better on most of the real problems compared to other popular classifiers.

Keywords: Kernel Methods; Pair-wise Classification; Support Vector Machine; Tumor Classification (search for similar items in EconPapers)
JEL-codes: C45 C61 C63 (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:anm:alpnmr:v:5:y:2017:i:2:p:283-292

DOI: 10.17093/alphanumeric.345115

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