Linear classifiers and selection of informative features

Abstract

In this work, to construct classifiers for two linearly inseparable sets, the problem of minimizing the margin of incorrect classification is formulated, approaches to achieving approximate solution, and calculation estimates of the optimal value for this problem, are considered. Results of computational experiments that compare proposed approaches with SVM are presented. The problem of identifying informative features for large-dimensional diagnostic applications is analyzed and algorithms for its solution are developed.