AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (690.7 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Unsupervised Classification by Iterative Voting

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

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.

References

[1]

L. Duan, S. Oyama, H. Sato, and M. Kurihara, Separate or joint? Estimation of multiple labels from crowdsourced annotations, Expert Systems with Applications, vol. 41, no. 13, pp. 5723–5732, 2014.

[2]

C. -M. Chiu, T. -P. Liang, and E. Turban, What can crowdsourcing do for decision support, Decision Support Systems, vol. 65, pp. 40–49, 2014.

[3]

V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, and L. Moy, Learning from crowds, J. Machine Learning Research, vol. 11, pp. 1297–1322, 2010.

[4]
J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. Movellan, Whose vote should count more: Optimal integration of labels from labelers of unknown expertise, in Proc. 22nd International Conf. Neural Information Processing Systems, Vancouver, Canada, 2009, pp. 2035–2043.
[5]

A. P. Dawid and A. M. Skene, Maximum likelihood estimation of observer error-rates using the EM algorithm, J. Royal Stat. Soc. Series C, vol. 28, no. 1, pp. 20–28, 1979.

[6]
V. B. Sinha, S. Rao, and V. N. Balasubramanian, Fast Dawid-Skene: A fast vote aggregation scheme for sentiment classification, presented at 7th KDD Workshop on Issues of Sentiment Discovery and Opinion Mining (KDD WISDOM 2018), London, UK, 2018.
[7]

D. Gale and L. S. Shapley, College admissions and the stability of marriage, American Mathematical Monthly, vol. 120, no. 5, pp. 386–391, 2013.

[8]
Toloka aggregation relevance 5, https://research.yandex.com/datasets/toloka, 2022.
[9]
P. Welinder, S. Branson, S. Belongie, and P. Perona, The multidimensional wisdom of crowds, in Proc. 23rd International Conf. on Neural Information Processing Systems, Vancouver, Canada, 2010, pp. 2424–2432.
[10]

F. Galton, One vote, one value, Nature, vol. 75, p. 414, 1907.

[11]

F. Galton, Vox populi, Nature, vol. 75, pp. 450–451, 1907.

[12]
A. Ghanaiem, Unsupervised collaborative classification under uncertainty, MSc dissertation, Department of Industrial Engineering, Tel-Aviv University, Tel Aviv, Israel, 2020.
[13]
N. Ratner, E. Kagan, P. Kumar, and I. Ben-Gal, Unsupervised classification for uncertain varying responces: The wisdom-in-the-crowd (WICRO) algorithm, Knowledge-Based Systems, https://doi.org/10.1016/j.knosys.2023.110551, 2023.
International Journal of Crowd Science
Pages 63-67
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

7207

Views

1185

Downloads

1

Crossref

1

Scopus

Altmetrics

Received: 15 October 2022
Revised: 12 December 2022
Accepted: 30 December 2022
Published: 22 June 2023
© The author(s) 2023.

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/).

Return