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SRCS (version 1.0)

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

1.0

License

LGPL (>= 3)

Maintainer

Pablo Villacorta

Last Published

January 1st, 2015

Functions in SRCS (1.0)

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

Performance of 6 different preprocessing algorithms over 6 supervised classification algorithms in imbalanced datasets

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

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

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

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

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

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