Unsupervised Classification by Iterative Voting

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.