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JOURNALS // Avtomatika i Telemekhanika // Archive

Avtomat. i Telemekh., 2022 Issue 12, Pages 156–168 (Mi at16101)

This article is cited in 2 papers

Intellectual Control Systems, Data Analysis

A machine learning method to reveal closed sets of common features of objects using constraint programming

A. A. Zuenko

Kola Science Centre of the Russian Academy of Sciences, Apatity, Murmansk oblast, 184209 Russia

Abstract: To solve machine learning problems, we have developed a method to identify closed sets of common features of objects (patterns) of the training sample. The novelty of the method lies in the fact that it is implemented within the concept of constraint programming and uses a new type of table constraints — compressed tables of the $D $-type — for internal representation and processing of the training sample. Search reduction is achieved by applying the proposed method of branching the search tree and using partial order relations on sets of objects (features) to prune unpromising branches. The method has a computational complexity estimate that for some types of input data is better than the estimates obtained for the studied prototypes.

Keywords: machine learning, constraint programming, table constraint, closed pattern, formal concept.

Presented by the member of Editorial Board: O. P. Kuznetsov

Received: 26.01.2022
Revised: 02.06.2022
Accepted: 28.07.2022

DOI: 10.31857/S000523102212011X


 English version:
Automation and Remote Control, 2022, 83:12, 1995–2005

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