Classification of Gene Samples Using Pair-Wise Support Vector Machines

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.

Identification of the Thief Zone Using a Support Vector Machine Method

“…As a classification method proposed by Vapnik et al, support vector machine [35] is mainly applied in the field of pattern recognition and has many unique advantages in small-sample, nonlinear, and high-dimensional pattern recognition. In recent years, it has been successfully applied in image recognition [36], signal processing [37], gene map recognition [38], and benign and malignant tumor recognition [39], showing its advantages. Moreover, field data are characterized by extremely complex relationships and few records, which is suitable for classification prediction with support vector machines.…”