Optimisation of multiclass supervised classification based on using output codes with error-correcting

Abstract

An approach of solving the problem of multiclass supervised classification, based on using errorcorrecting codes is considered. The main problem here is the creation of binary code matrix, which provides high classification accuracy. Binary classifiers must be distinct and accurate. In this issue, there are many questions. What should be the elements of the matrix, how many elements provide the best accuracy and how to find them? In this paper an approach to solve some optimization problems for the construction of the binary code matrix is considered. The problem of finding the best binary classifiers (columns of matrix) is formulated as a discrete optimization problem. For some partial precedent classification approach, there is a calculation of the effective values of optimising function. Prospects of this approach are confirmed by a series of experiments on various practical tasks.