@misc{10481/87039,
year = {2015},
month = {6},
url = {https://hdl.handle.net/10481/87039},
abstract = {The problem of comparing a new solution method against existing ones to find statistically
significant differences arises very often in sciences and engineering. When the problem instance being
solved is defined by several parameters, assessing a number of methods with respect to many problem
configurations simultaneously becomes a hard task. Some visualization technique is required for
presenting a large number of statistical significance results in an easily interpretable way. Here we
review an existing color-based approach called Statistical Ranking Color Scheme (SRCS) for displaying
the results of multiple pairwise statistical comparisons between several methods assessed separately on
a number of problem configurations. We introduce an R package implementing SRCS, which performs
all the pairwise statistical tests from user data and generates customizable plots. We demonstrate
its applicability on two examples from the areas of dynamic optimization and machine learning, in
which several algorithms are compared on many problem instances, each defined by a combination of
parameters},
organization = {Department of Computer Science and Artificial Intelligence, Universidad de Granada},
publisher = {The R Foundation},
keywords = {Statistical hypothesis testing},
keywords = {Machine Learning},
title = {SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R},
doi = {10.32614/RJ-2015-023},
author = {Villacorta Iglesias, Pablo José and Sáez Muñoz, José Antonio},
}