This article is cited in 3 papers

On the choice of partial orders on feature values sets in the supervised classification problem

E. V. Djukova, G. O. Masliakov

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation

Abstract: The authors consider one of the central problems of machine learning — the supervised classification. A scheme for the logical classification algorithms synthesis is described under the assumption that the features descriptions of precedents are the elements of the finite partial orders Cartesian product. A criterion for the correctness of the voting algorithm of representative elementary classifiers is formulated. The authors study the possibility of defining linear orders on sets of feature values that provide better classification, which is not necessarily correct, in assumption that the source data are not ordered (the precedents descriptions are the elements of the antichains product). A procedure is proposed for “correct” consistent ordering of the acceptable values of separate features, while the remaining features are antichains. The results of experiments on real data are presented demonstrating the effectiveness of the methods developed in the work.

Keywords: machine learning, logical classification algorithms, correct supervised classification algorithm, partially ordered set, Cartesian product of partial orders, linear order, dualization over product of partial orders.

Received: 15.01.2021

DOI: 10.14357/19922264210410