Unsupervised Classification under Uncertainty: The Distance-Based Algorithm

Alaa Ghanaiem, Evgeny Kagan (), Parteek Kumar, Tal Raviv, Peter Glynn and Irad Ben-Gal
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Alaa Ghanaiem: Department of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv 69978, Israel
Evgeny Kagan: Department of Industrial Engineering and Management, Faculty of Engineering, Ariel University, Ariel 40700, Israel
Parteek Kumar: Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India
Tal Raviv: Department of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv 69978, Israel
Peter Glynn: Department of Management Science and Engineering, Institute of Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA
Irad Ben-Gal: Department of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Tel-Aviv 69978, Israel

Mathematics, 2023, vol. 11, issue 23, 1-19

Abstract: This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting (“wisdom of the crowd”) and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents’ expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents’ classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods.

Keywords: classification; uncertainty; collective choice; likelihood (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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