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SRCS (version 1.1)

Statistical Ranking Color Scheme for Multiple Pairwise Comparisons

Description

Implementation of the SRCS method for a color-based visualization of the results of multiple pairwise tests on a large number of problem configurations, proposed in: I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.

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Version

Install

install.packages('SRCS')

Monthly Downloads

147

Version

1.1

License

LGPL (>= 3)

Maintainer

Pablo Villacorta

Last Published

July 2nd, 2015

Functions in SRCS (1.1)

ML1

Performance of 6 different supervised classification algorithms on eight noisy datasets (see references)
SRCSranks

Computes the ranks of all the algorithms from their (repeated) results measurements after grouping them by several factors combined simultaneosly.
MPBall

Performance of 3 different dynamic optimization algorithms on the Moving Peaks Benchmark captured at five time moments of the execution (see references)
SRCScomparison

Compares the performance of two algorithms for a single problem configuration specified by the user.
SRCS

R package implementing the Statistical Ranking Color Scheme for visualizing the results of multiple parameterized pairwise comparisons.
MPB

Performance of 8 different dynamic optimization algorithms on the Moving Peaks Benchmark (see references)
plot.SRCS

Heatmap plot of the ranking achieved by a target variable levels after all statistical pairwise comparisons in multi-parameter problem instances.