  Employing multiple workers to label data for machine learning models has
become increasingly important in recent years with greater demand to collect
huge volumes of labelled data to train complex models while mitigating the risk
of incorrect and noisy labelling. Whether it is large scale data gathering on
popular crowd-sourcing platforms or smaller sets of workers in high-expertise
labelling exercises, there are various methods recommended to gather a
consensus from employed workers and establish ground-truth labels. However,
there is very little research on how the various parameters of a labelling task
can impact said methods. These parameters include the number of workers, worker
expertise, number of labels in a taxonomy and sample size. In this paper,
Majority Vote, CrowdTruth and Binomial Expectation Maximisation are
investigated against the permutations of these parameters in order to provide
better understanding of the parameter settings to give an advantage in
ground-truth inference. Findings show that both Expectation Maximisation and
CrowdTruth are only likely to give an advantage over majority vote under
certain parameter conditions, while there are many cases where the methods can
be shown to have no major impact. Guidance is given as to what parameters
methods work best under, while the experimental framework provides a way of
testing other established methods and also testing new methods that can attempt
to provide advantageous performance where the methods in this paper did not. A
greater level of understanding regarding optimal crowd-sourcing parameters is
also achieved.
