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International Journal of Crowd Science 2023, 7(2): 63-67 https://doi.org/10.26599/IJCS.2022.9100037
Open Access | Issue | Published: 22 June 2023

Unsupervised Classification by Iterative Voting

Show Author's Information Evgeny Kagan1( ),  Alexander Novoselsky2
Department of Industrial Engineering, Ariel University, Ariel 40700, Israel
Weizmann Institute of Science, Rehovot 76100, Israel
Keywords:
decision making, uncertainty, unsupervised classification, plurality voting
Cite this article:
Kagan E, Novoselsky A. Unsupervised Classification by Iterative Voting. International Journal of Crowd Science, 2023, 7(2): 63-67. https://doi.org/10.26599/IJCS.2022.9100037
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Abstract Full text About this article

In the paper we present a simple algorithm for unsupervised classification of given items by a group of agents. The purpose of the algorithm is to provide fast and computationally light solutions of classification tasks by the randomly chosen agents. The algorithm follows basic techniques of plurality voting and combinatorial stable matching and does not use additional assumptions or information about the levels of the agents’ expertise. Performance of the suggested algorithm is illustrated by its application to simulated and real-world datasets, and it was demonstrated that the algorithm provides close to correct classifications. The obtained solutions can be used both separately and as initial classifications in more complicated algorithms.


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About this article

Unsupervised Classification by Iterative Voting

Show Author's information Evgeny Kagan1( ), Alexander Novoselsky2
Department of Industrial Engineering, Ariel University, Ariel 40700, Israel
Weizmann Institute of Science, Rehovot 76100, Israel

Abstract

In the paper we present a simple algorithm for unsupervised classification of given items by a group of agents. The purpose of the algorithm is to provide fast and computationally light solutions of classification tasks by the randomly chosen agents. The algorithm follows basic techniques of plurality voting and combinatorial stable matching and does not use additional assumptions or information about the levels of the agents’ expertise. Performance of the suggested algorithm is illustrated by its application to simulated and real-world datasets, and it was demonstrated that the algorithm provides close to correct classifications. The obtained solutions can be used both separately and as initial classifications in more complicated algorithms.

Keywords: decision making, uncertainty, unsupervised classification, plurality voting

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Received: 15 October 2022
Revised: 12 December 2022
Accepted: 30 December 2022
Published: 22 June 2023
Issue date: June 2023

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© The author(s) 2023.

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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