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JOURNALS // Artificial Intelligence and Decision Making // Archive

Artificial Intelligence and Decision Making, 2020 Issue 2, Pages 63–77 (Mi iipr135)

This article is cited in 1 paper

Decision analysis

Shortening dimensionality of attribute space: method SOCRATE

A. B. Petrovskiiabcd

a Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
b Belgorod Shukhov State Technological University, Belgorod, Russia
c National Research University "Belgorod State University", Belgorod, Russia
d Volgograd State Technical University, Volgograd, Russia

Abstract: A new method SOCRATES (ShOrtening Criteria and ATtributES) to reduce the dimensionality of attribute space is described. In the method, a lot of initial numerical and/or verbal characteristics of objects are aggregated into a single integral index or several composite indicators with small scales of qualitative estimates. Multi-attribute objects are represented as multisets of object properties. Aggregating indicators includes various methods for a transformation of attributes and their scales. Reducing the number of attributes and shortening their scales allows us to simplify the solution of applied problems, in particular, problems of multiple criteria choice, and explain the obtained results.

Keywords: multi-attribute objects, multisets, attribute space, dimensionality reduction, aggregation of attributes, composite indicator, multiple criteria choice.

DOI: 10.14357/20718594200205


 English version:
, 2021, 48:5, 342–355

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© Steklov Math. Inst. of RAS, 2024