Comparison of classifiers in challenge scheme
Sergio Nava-Muñoz Mario Graff Guerrero Hugo Jair Escalante
In recent decades, challenges have become very popular in scientific research
as these are crowdsourcing schemes. In particular, challenges are essential for
developing machine learning algorithms. For the challenges settings, it is
vital to establish the scientific question, the dataset (with adequate quality,
quantity, diversity, and complexity), performance metrics, as well as a way to
authenticate the participants' results (Gold Standard). This paper addresses
the problem of evaluating the performance of different competitors (algorithms)
under the restrictions imposed by the challenge scheme, such as the comparison
of multiple competitors with a unique dataset (with fixed size), a minimal
number of submissions and, a set of metrics chosen to assess performance. The
algorithms are sorted according to the performance metric. Still, it is common
to observe performance differences among competitors as small as hundredths or
even thousandths, so the question is whether the differences are significant.
This paper analyzes the results of the MeOffendEs@IberLEF 2021 competition and
proposes to make inference through resampling techniques (bootstrap) to support
Challenge organizers' decision-making.