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MATLAB code to calculate a new version of the Signed Rank Test for comparing two classifiers on multiple datasets, as explained in a paper by Benavoli et al. (2017). The code is adapted from the python library baycomp.

LucyKuncheva/Bayesian-Analysis-for-Comparing-Classifiers

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Bayesian-Analysis-for-Comparing-Classifiers

This is a small MATLAB implementation of some of the functionalities of Janez Demsar’s Python library baycomp available here: https://github.com/janezd/baycomp. The code can be used for comparing two classifiers using the Signed Rank Test proposed in the paper: A. Benavoli, G. Corani, J. Demsar, M. Zaffalon, "Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis", Journal of Machine Learning Research 18 (2017) 1-36.

The repository contains the following files:

  • BayesianAnalyisExample.m This is an example that runs the Bayesian Analysis exaplained in the paper and reproduces

    • Figure 10 (the histogram of theta_left = probability that NBC << AODE)

    • and Figure 11 (the triangle with the point cloud)

  • NBC_AODE_differences.xlsx This is an excel file with the differences between classifiers NBC (Naive Bayes) and AODE (averaged one-dependence estimator). The values are taken from Table 7 in the paper.

  • signed_rank_test_diff.m This is a function that takes a vector of differences of classifier accuracies across multiple data sets (A-B) and calculates the results of the signed rank test. The function is largely translated from the respective component of the baycomp library. It outputs an array with probability density values (theta_left, theta_rope, theta_right) needed for the triangle plot and the set of three probabilities p_left (classifier A is better), p_rope (the two classifiers are equivalent) and p_right (classifier B is better).

  • barycentric_plot.m This code is used to plot the triangle diagram with the cloud of simulated points in barycentric coordinates.

  • density_plot.m This is a function that produces a scatterplot in the current axes, where each point is coloured depending on the density of its neighbourhood.

  • two_classifiers_one_dataset This function computes probabilities using a Bayesian correlated t-test. It was written directly in MATLAB using the text of the paper [Benavoli et al., 2017]. The input is a vector of differences of classifier accuracies across one or repeated cross-validation experiment (A-B). The function outputs an array with probabilities p_left (classifier A is better), p_rope (the two classifiers are equivalent) and p_right (classifier B is better). It also outputs the x-y values for the density plot.

@misc{KunchevaMATLABBayesian2020,
author = {Ludmila I Kuncheva},
title = {Bayesian-Analysis-for-Comparing-Classifiers},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LucyKuncheva/Bayesian-Analysis-for-Comparing-Classifiers}}
}

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MATLAB code to calculate a new version of the Signed Rank Test for comparing two classifiers on multiple datasets, as explained in a paper by Benavoli et al. (2017). The code is adapted from the python library baycomp.

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