2008:Audio Genre Classification Results

Introduction

These are the results for the 2008 running of the Audio Genre Classification task. For background information about this task set please refer to the 2008:Audio Genre Classification page.

General Legend

Team ID

CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
GP1 = G. Peeters
GT1 (mono) = G. Tzanetakis
GT2 (stereo) = G. Tzanetakis
GT3 (multicore) = G. Tzanetakis
LRPPI1 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4
ME1 = I. M. Mandel, D. P. W. Ellis 1
ME2 = I. M. Mandel, D. P. W. Ellis 2
ME3 = I. M. Mandel, D. P. W. Ellis 3

Overall Summary Results

Task 1 (MIXED) Results

MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds

Participant	Average Classifcation Accuracy
CL1	62.04%
CL2	63.39%
GP1	63.90%
GT1	64.71%
GT2	66.41%
GT3	65.62%
LRPPI1	65.06%
LRPPI2	62.26%
LRPPI3	60.84%
LRPPI4	60.46%
ME1	65.41%
ME2	65.30%
ME3	65.20%

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Accuracy Across Folds

Classification fold	CL1	CL2	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
0	0.592	0.598	0.634	0.639	0.642	0.654	0.650	0.610	0.598	0.606	0.631	0.631	0.628
1	0.644	0.661	0.634	0.651	0.682	0.664	0.669	0.637	0.626	0.617	0.668	0.665	0.666
2	0.625	0.643	0.649	0.652	0.669	0.651	0.633	0.622	0.602	0.592	0.663	0.662	0.663

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Accuracy Across Categories

Class	CL1	CL2	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
BAROQUE	0.616	0.637	0.750	0.669	0.724	0.673	0.673	0.660	0.666	0.629	0.754	0.759	0.757
BLUES	0.711	0.741	0.674	0.690	0.677	0.701	0.700	0.703	0.713	0.689	0.713	0.706	0.706
CLASSICAL	0.608	0.598	0.592	0.559	0.649	0.606	0.563	0.603	0.559	0.524	0.666	0.669	0.672
COUNTRY	0.624	0.596	0.697	0.793	0.830	0.679	0.669	0.640	0.621	0.617	0.660	0.656	0.653
EDANCE	0.560	0.591	0.536	0.590	0.624	0.648	0.672	0.626	0.646	0.686	0.657	0.649	0.639
JAZZ	0.679	0.699	0.606	0.627	0.682	0.626	0.640	0.602	0.566	0.574	0.679	0.676	0.680
METAL	0.677	0.709	0.750	0.713	0.656	0.733	0.707	0.642	0.623	0.643	0.612	0.627	0.629
RAPHIPHOP	0.809	0.823	0.873	0.846	0.846	0.854	0.860	0.837	0.826	0.848	0.841	0.836	0.837
ROCKROLL	0.420	0.418	0.406	0.384	0.414	0.447	0.448	0.391	0.377	0.391	0.450	0.450	0.448
ROMANTIC	0.501	0.527	0.508	0.602	0.540	0.597	0.574	0.523	0.488	0.444	0.510	0.505	0.500

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MIREX 2008 Audio Genre Classification Run Times

Participant	Runtime (hh:mm) / Fold
CL1	Feat Ex: 01:29 Train/Classify: 0:33
CL2	Feat Ex: 01:31 Train/Classify: 01:01
GP1	Feat Ex: 11:37 Train/Classify: 00:25
GT1	Feat Ex/Train/Classify: 00:36
GT2	Feat Ex/Train/Classify: 00:35
GT3	Feat Ex: 00:12 Train/Classify: 00:01
LRPPI1	Feat Ex: 28:50 Train/Classify: 00:02
LRPPI2	Feat Ex: 28:50 Train/Classify: 00:17
LRPPI3	Feat Ex: 28:50 Train/Classify: 00:20
LRPPI4	Feat Ex: 28:50 Train/Classify: 00:35
ME1	Feat Ex: 3:35 Train/Classify: 00:02
ME2	Feat Ex: 3:35 Train/Classify: 00:02
ME3	Feat Ex: 3:35 Train/Classify: 00:02

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CSV Files Without Rounding

audiogenre_results_fold.csv
audiogenre_results_class.csv

Results By Algorithm

(.tar.gz)
CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
LRPPI1 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4
ME1 = I. M. Mandel, D. P. W. Ellis 1
ME2 = I. M. Mandel, D. P. W. Ellis 2
ME3 = I. M. Mandel, D. P. W. Ellis 3
GP = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis

Task 2 (LATIN) Results

MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds

Participant	Average Classifcation Accuracy
CL1	65.17%
CL2	64.04%
GP1	62.72%
GT1	53.65%
GT2	53.79%
GT3	53.67%
LRPPI1	58.64%
LRPPI2	62.23%
LRPPI3	59.55%
LRPPI4	59.00%
ME1	54.15%
ME2	54.70%
ME3	54.99%

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Accuracy Across Folds

Classification fold	CL1	CL2	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
0	0.755	0.750	0.694	0.674	0.677	0.657	0.661	0.697	0.671	0.680	0.681	0.684	0.685
1	0.541	0.528	0.553	0.435	0.435	0.422	0.512	0.573	0.526	0.506	0.403	0.409	0.415
2	0.660	0.644	0.634	0.501	0.502	0.531	0.587	0.597	0.590	0.585	0.541	0.548	0.550

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Accuracy Across Categories

Class	CL1	CL2	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
axe	0.753	0.745	0.558	0.637	0.640	0.695	0.529	0.560	0.537	0.528	0.679	0.679	0.681
bachata	0.957	0.622	0.969	0.595	0.592	0.587	0.957	0.950	0.956	0.957	0.932	0.935	0.935
bolero	0.630	0.633	0.768	0.702	0.705	0.746	0.683	0.726	0.646	0.668	0.664	0.662	0.666
forro	0.356	0.335	0.270	0.146	0.145	0.127	0.258	0.342	0.292	0.287	0.174	0.186	0.188
gaucha	0.501	0.491	0.345	0.348	0.348	0.299	0.345	0.357	0.327	0.338	0.435	0.436	0.434
merengue	0.895	0.898	0.897	0.812	0.806	0.784	0.847	0.794	0.825	0.833	0.698	0.699	0.728
pagode	0.355	0.368	0.303	0.307	0.297	0.249	0.322	0.391	0.361	0.318	0.231	0.240	0.243
salsa	0.886	0.850	0.750	0.715	0.719	0.668	0.788	0.793	0.769	0.766	0.698	0.710	0.716
sertaneja	0.209	0.205	0.200	0.159	0.186	0.212	0.132	0.227	0.210	0.170	0.090	0.090	0.094
tango	0.590	0.587	0.585	0.588	0.588	0.582	0.592	0.581	0.586	0.590	0.588	0.588	0.588

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MIREX 2008 Audio Genre Classification Run Times

Participant	Runtime (hh:mm) / Fold
CL1	Feat Ex: 00:47 Train/Classify: 0:13
CL2	Feat Ex: 00:48 Train/Classify: 00:23
GP1	Feat Ex: 07:12 Train/Classify: 00:15
GT1	Feat Ex/Train/Classify: 00:16
GT2	Feat Ex/Train/Classify: 00:17
GT3	Feat Ex: 00:06 Train/Classify: 00:00 (6 sec)
LRPPI1	Feat Ex: 15:33 Train/Classify: 00:01
LRPPI2	Feat Ex: 15:33 Train/Classify: 00:06
LRPPI3	Feat Ex: 15:33 Train/Classify: 00:06
LRPPI4	Feat Ex: 15:33 Train/Classify: 00:11
ME1	Feat Ex: 1:58 Train/Classify: 00:00 (28 sec)
ME2	Feat Ex: 1:58 Train/Classify: 00:00 (28 sec)
ME3	Feat Ex: 1:58 Train/Classify: 00:00 (28 sec)

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CSV Files Without Rounding

audiolatin_results_fold.csv
audiolatin_results_class.csv

Results By Algorithm

(.tar.gz)
CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
LRPPI1 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 4
ME1 = I. M. Mandel, D. P. W. Ellis 1
ME2 = I. M. Mandel, D. P. W. Ellis 2
ME3 = I. M. Mandel, D. P. W. Ellis 3

Friedman's Test for Significant Differences

Task 1 (Mixed) Classes vs. Systems

The Friedman test was run in MATLAB against the average accuracy for each class.

Friedman's Anova Table

Source	SS	df	MS	Chi-sq	Prob>Chi-sq
Columns	243.6	10	24.36	22.15	0.0144
Error	856.4	90	9.5156
Total	1100	109

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
CL1	CL2	-5.5740	-0.8000	3.9740	FALSE
CL1	GP1	-5.1740	-0.4000	4.3740	FALSE
CL1	GT1	-5.0740	-0.3000	4.4740	FALSE
CL1	GT2	-3.3740	1.4000	6.1740	FALSE
CL1	GT3	-3.5740	1.2000	5.9740	FALSE
CL1	LRPPI1	-3.4740	1.3000	6.0740	FALSE
CL1	LRPPI2	-2.3740	2.4000	7.1740	FALSE
CL1	LRPPI3	-2.4740	2.3000	7.0740	FALSE
CL1	LRPPI4	-1.1740	3.6000	8.3740	FALSE
CL1	ME1	-1.1740	3.6000	8.3740	FALSE
CL2	GP1	-4.3740	0.4000	5.1740	FALSE
CL2	GT1	-4.2740	0.5000	5.2740	FALSE
CL2	GT2	-2.5740	2.2000	6.9740	FALSE
CL2	GT3	-2.7740	2.0000	6.7740	FALSE
CL2	LRPPI1	-2.6740	2.1000	6.8740	FALSE
CL2	LRPPI2	-1.5740	3.2000	7.9740	FALSE
CL2	LRPPI3	-1.6740	3.1000	7.8740	FALSE
CL2	LRPPI4	-0.3740	4.4000	9.1740	FALSE
CL2	ME1	-0.3740	4.4000	9.1740	FALSE
GP1	GT1	-4.6740	0.1000	4.8740	FALSE
GP1	GT2	-2.9740	1.8000	6.5740	FALSE
GP1	GT3	-3.1740	1.6000	6.3740	FALSE
GP1	LRPPI1	-3.0740	1.7000	6.4740	FALSE
GP1	LRPPI2	-1.9740	2.8000	7.5740	FALSE
GP1	LRPPI3	-2.0740	2.7000	7.4740	FALSE
GP1	LRPPI4	-0.7740	4.0000	8.7740	FALSE
GP1	ME1	-0.7740	4.0000	8.7740	FALSE
GT1	GT2	-3.0740	1.7000	6.4740	FALSE
GT1	GT3	-3.2740	1.5000	6.2740	FALSE
GT1	LRPPI1	-3.1740	1.6000	6.3740	FALSE
GT1	LRPPI2	-2.0740	2.7000	7.4740	FALSE
GT1	LRPPI3	-2.1740	2.6000	7.3740	FALSE
GT1	LRPPI4	-0.8740	3.9000	8.6740	FALSE
GT1	ME1	-0.8740	3.9000	8.6740	FALSE
GT2	GT3	-4.9740	-0.2000	4.5740	FALSE
GT2	LRPPI1	-4.8740	-0.1000	4.6740	FALSE
GT2	LRPPI2	-3.7740	1.0000	5.7740	FALSE
GT2	LRPPI3	-3.8740	0.9000	5.6740	FALSE
GT2	LRPPI4	-2.5740	2.2000	6.9740	FALSE
GT2	ME1	-2.5740	2.2000	6.9740	FALSE
GT3	LRPPI1	-4.6740	0.1000	4.8740	FALSE
GT3	LRPPI2	-3.5740	1.2000	5.9740	FALSE
GT3	LRPPI3	-3.6740	1.1000	5.8740	FALSE
GT3	LRPPI4	-2.3740	2.4000	7.1740	FALSE
GT3	ME1	-2.3740	2.4000	7.1740	FALSE
LRPPI1	LRPPI2	-3.6740	1.1000	5.8740	FALSE
LRPPI1	LRPPI3	-3.7740	1.0000	5.7740	FALSE
LRPPI1	LRPPI4	-2.4740	2.3000	7.0740	FALSE
LRPPI1	ME1	-2.4740	2.3000	7.0740	FALSE
LRPPI2	LRPPI3	-4.8740	-0.1000	4.6740	FALSE
LRPPI2	LRPPI4	-3.5740	1.2000	5.9740	FALSE
LRPPI2	ME1	-3.5740	1.2000	5.9740	FALSE
LRPPI3	LRPPI4	-3.4740	1.3000	6.0740	FALSE
LRPPI3	ME1	-3.4740	1.3000	6.0740	FALSE
LRPPI4	ME1	-4.7740	0.0000	4.7740	FALSE

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Task 1 (Mixed) Folds vs. Systems

The Friedman test was run in MATLAB against the accuracy for each fold.

Friedman's Anova Table

Source	SS	df	MS	Chi-sq	Prob>Chi-sq
Columns	255.333	10	25.5333	23.21	0.01
Error	74.667	20	3.7333
Total	330	32

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
CL1	CL2	-7.3828	1.3333	10.0495	FALSE
CL1	GP1	-6.7162	2.0000	10.7162	FALSE
CL1	GT1	-6.7162	2.0000	10.7162	FALSE
CL1	GT2	-6.0495	2.6667	11.3828	FALSE
CL1	GT3	-4.0495	4.6667	13.3828	FALSE
CL1	LRPPI1	-3.7162	5.0000	13.7162	FALSE
CL1	LRPPI2	-2.3828	6.3333	15.0495	FALSE
CL1	LRPPI3	-1.7162	7.0000	15.7162	FALSE
CL1	LRPPI4	-0.3828	8.3333	17.0495	FALSE
CL1	ME1	-0.3828	8.3333	17.0495	FALSE
CL2	GP1	-8.0495	0.6667	9.3828	FALSE
CL2	GT1	-8.0495	0.6667	9.3828	FALSE
CL2	GT2	-7.3828	1.3333	10.0495	FALSE
CL2	GT3	-5.3828	3.3333	12.0495	FALSE
CL2	LRPPI1	-5.0495	3.6667	12.3828	FALSE
CL2	LRPPI2	-3.7162	5.0000	13.7162	FALSE
CL2	LRPPI3	-3.0495	5.6667	14.3828	FALSE
CL2	LRPPI4	-1.7162	7.0000	15.7162	FALSE
CL2	ME1	-1.7162	7.0000	15.7162	FALSE
GP1	GT1	-8.7162	0.0000	8.7162	FALSE
GP1	GT2	-8.0495	0.6667	9.3828	FALSE
GP1	GT3	-6.0495	2.6667	11.3828	FALSE
GP1	LRPPI1	-5.7162	3.0000	11.7162	FALSE
GP1	LRPPI2	-4.3828	4.3333	13.0495	FALSE
GP1	LRPPI3	-3.7162	5.0000	13.7162	FALSE
GP1	LRPPI4	-2.3828	6.3333	15.0495	FALSE
GP1	ME1	-2.3828	6.3333	15.0495	FALSE
GT1	GT2	-8.0495	0.6667	9.3828	FALSE
GT1	GT3	-6.0495	2.6667	11.3828	FALSE
GT1	LRPPI1	-5.7162	3.0000	11.7162	FALSE
GT1	LRPPI2	-4.3828	4.3333	13.0495	FALSE
GT1	LRPPI3	-3.7162	5.0000	13.7162	FALSE
GT1	LRPPI4	-2.3828	6.3333	15.0495	FALSE
GT1	ME1	-2.3828	6.3333	15.0495	FALSE
GT2	GT3	-6.7162	2.0000	10.7162	FALSE
GT2	LRPPI1	-6.3828	2.3333	11.0495	FALSE
GT2	LRPPI2	-5.0495	3.6667	12.3828	FALSE
GT2	LRPPI3	-4.3828	4.3333	13.0495	FALSE
GT2	LRPPI4	-3.0495	5.6667	14.3828	FALSE
GT2	ME1	-3.0495	5.6667	14.3828	FALSE
GT3	LRPPI1	-8.3828	0.3333	9.0495	FALSE
GT3	LRPPI2	-7.0495	1.6667	10.3828	FALSE
GT3	LRPPI3	-6.3828	2.3333	11.0495	FALSE
GT3	LRPPI4	-5.0495	3.6667	12.3828	FALSE
GT3	ME1	-5.0495	3.6667	12.3828	FALSE
LRPPI1	LRPPI2	-7.3828	1.3333	10.0495	FALSE
LRPPI1	LRPPI3	-6.7162	2.0000	10.7162	FALSE
LRPPI1	LRPPI4	-5.3828	3.3333	12.0495	FALSE
LRPPI1	ME1	-5.3828	3.3333	12.0495	FALSE
LRPPI2	LRPPI3	-8.0495	0.6667	9.3828	FALSE
LRPPI2	LRPPI4	-6.7162	2.0000	10.7162	FALSE
LRPPI2	ME1	-6.7162	2.0000	10.7162	FALSE
LRPPI3	LRPPI4	-7.3828	1.3333	10.0495	FALSE
LRPPI3	ME1	-7.3828	1.3333	10.0495	FALSE
LRPPI4	ME1	-8.7162	0.0000	8.7162	FALSE

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Task 2 (Latin) Classes vs. Systems

The Friedman test was run in MATLAB against the average accuracy for each class.

Friedman's Anova Table

Source	SS	df	MS	Chi-sq	Prob>Chi-sq
Columns	235	10	23.5	21.38	0.0186
Error	864	90	9.6
Total	1099	109

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
CL1	CL2	-3.7219	1.0500	5.8219	FALSE
CL1	GP1	-3.2219	1.5500	6.3219	FALSE
CL1	GT1	-2.3219	2.4500	7.2219	FALSE
CL1	GT2	-1.7219	3.0500	7.8219	FALSE
CL1	GT3	-1.7719	3.0000	7.7719	FALSE
CL1	LRPPI1	-2.1219	2.6500	7.4219	FALSE
CL1	LRPPI2	-0.2219	4.5500	9.3219	FALSE
CL1	LRPPI3	-0.7719	4.0000	8.7719	FALSE
CL1	LRPPI4	-0.6719	4.1000	8.8719	FALSE
CL1	ME1	0.1781	4.9500	9.7219	TRUE
CL2	GP1	-4.2719	0.5000	5.2719	FALSE
CL2	GT1	-3.3719	1.4000	6.1719	FALSE
CL2	GT2	-2.7719	2.0000	6.7719	FALSE
CL2	GT3	-2.8219	1.9500	6.7219	FALSE
CL2	LRPPI1	-3.1719	1.6000	6.3719	FALSE
CL2	LRPPI2	-1.2719	3.5000	8.2719	FALSE
CL2	LRPPI3	-1.8219	2.9500	7.7219	FALSE
CL2	LRPPI4	-1.7219	3.0500	7.8219	FALSE
CL2	ME1	-0.8719	3.9000	8.6719	FALSE
GP1	GT1	-3.8719	0.9000	5.6719	FALSE
GP1	GT2	-3.2719	1.5000	6.2719	FALSE
GP1	GT3	-3.3219	1.4500	6.2219	FALSE
GP1	LRPPI1	-3.6719	1.1000	5.8719	FALSE
GP1	LRPPI2	-1.7719	3.0000	7.7719	FALSE
GP1	LRPPI3	-2.3219	2.4500	7.2219	FALSE
GP1	LRPPI4	-2.2219	2.5500	7.3219	FALSE
GP1	ME1	-1.3719	3.4000	8.1719	FALSE
GT1	GT2	-4.1719	0.6000	5.3719	FALSE
GT1	GT3	-4.2219	0.5500	5.3219	FALSE
GT1	LRPPI1	-4.5719	0.2000	4.9719	FALSE
GT1	LRPPI2	-2.6719	2.1000	6.8719	FALSE
GT1	LRPPI3	-3.2219	1.5500	6.3219	FALSE
GT1	LRPPI4	-3.1219	1.6500	6.4219	FALSE
GT1	ME1	-2.2719	2.5000	7.2719	FALSE
GT2	GT3	-4.8219	-0.0500	4.7219	FALSE
GT2	LRPPI1	-5.1719	-0.4000	4.3719	FALSE
GT2	LRPPI2	-3.2719	1.5000	6.2719	FALSE
GT2	LRPPI3	-3.8219	0.9500	5.7219	FALSE
GT2	LRPPI4	-3.7219	1.0500	5.8219	FALSE
GT2	ME1	-2.8719	1.9000	6.6719	FALSE
GT3	LRPPI1	-5.1219	-0.3500	4.4219	FALSE
GT3	LRPPI2	-3.2219	1.5500	6.3219	FALSE
GT3	LRPPI3	-3.7719	1.0000	5.7719	FALSE
GT3	LRPPI4	-3.6719	1.1000	5.8719	FALSE
GT3	ME1	-2.8219	1.9500	6.7219	FALSE
LRPPI1	LRPPI2	-2.8719	1.9000	6.6719	FALSE
LRPPI1	LRPPI3	-3.4219	1.3500	6.1219	FALSE
LRPPI1	LRPPI4	-3.3219	1.4500	6.2219	FALSE
LRPPI1	ME1	-2.4719	2.3000	7.0719	FALSE
LRPPI2	LRPPI3	-5.3219	-0.5500	4.2219	FALSE
LRPPI2	LRPPI4	-5.2219	-0.4500	4.3219	FALSE
LRPPI2	ME1	-4.3719	0.4000	5.1719	FALSE
LRPPI3	LRPPI4	-4.6719	0.1000	4.8719	FALSE
LRPPI3	ME1	-3.8219	0.9500	5.7219	FALSE
LRPPI4	ME1	-3.9219	0.8500	5.6219	FALSE

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Task 2 (Latin) Folds vs. Systems

The Friedman test was run in MATLAB against the accuracy for each fold.

Friedman's Anova Table

Source	SS	df	MS	Chi-sq	Prob>Chi-sq
Columns	265.833	10	26.5833	24.2	0.0071
Error	63.667	20	3.1833
Total	329.5	32

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
CL1	CL2	-7.7095	1.0000	9.7095	FALSE
CL1	GP1	-7.3762	1.3333	10.0429	FALSE
CL1	GT1	-7.7095	1.0000	9.7095	FALSE
CL1	GT2	-4.0429	4.6667	13.3762	FALSE
CL1	GT3	-3.7095	5.0000	13.7095	FALSE
CL1	LRPPI1	-3.0429	5.6667	14.3762	FALSE
CL1	LRPPI2	-2.3762	6.3333	15.0429	FALSE
CL1	LRPPI3	-1.8762	6.8333	15.5429	FALSE
CL1	LRPPI4	-0.3762	8.3333	17.0429	FALSE
CL1	ME1	-1.2095	7.5000	16.2095	FALSE
CL2	GP1	-8.3762	0.3333	9.0429	FALSE
CL2	GT1	-8.7095	0.0000	8.7095	FALSE
CL2	GT2	-5.0429	3.6667	12.3762	FALSE
CL2	GT3	-4.7095	4.0000	12.7095	FALSE
CL2	LRPPI1	-4.0429	4.6667	13.3762	FALSE
CL2	LRPPI2	-3.3762	5.3333	14.0429	FALSE
CL2	LRPPI3	-2.8762	5.8333	14.5429	FALSE
CL2	LRPPI4	-1.3762	7.3333	16.0429	FALSE
CL2	ME1	-2.2095	6.5000	15.2095	FALSE
GP1	GT1	-9.0429	-0.3333	8.3762	FALSE
GP1	GT2	-5.3762	3.3333	12.0429	FALSE
GP1	GT3	-5.0429	3.6667	12.3762	FALSE
GP1	LRPPI1	-4.3762	4.3333	13.0429	FALSE
GP1	LRPPI2	-3.7095	5.0000	13.7095	FALSE
GP1	LRPPI3	-3.2095	5.5000	14.2095	FALSE
GP1	LRPPI4	-1.7095	7.0000	15.7095	FALSE
GP1	ME1	-2.5429	6.1667	14.8762	FALSE
GT1	GT2	-5.0429	3.6667	12.3762	FALSE
GT1	GT3	-4.7095	4.0000	12.7095	FALSE
GT1	LRPPI1	-4.0429	4.6667	13.3762	FALSE
GT1	LRPPI2	-3.3762	5.3333	14.0429	FALSE
GT1	LRPPI3	-2.8762	5.8333	14.5429	FALSE
GT1	LRPPI4	-1.3762	7.3333	16.0429	FALSE
GT1	ME1	-2.2095	6.5000	15.2095	FALSE
GT2	GT3	-8.3762	0.3333	9.0429	FALSE
GT2	LRPPI1	-7.7095	1.0000	9.7095	FALSE
GT2	LRPPI2	-7.0429	1.6667	10.3762	FALSE
GT2	LRPPI3	-6.5429	2.1667	10.8762	FALSE
GT2	LRPPI4	-5.0429	3.6667	12.3762	FALSE
GT2	ME1	-5.8762	2.8333	11.5429	FALSE
GT3	LRPPI1	-8.0429	0.6667	9.3762	FALSE
GT3	LRPPI2	-7.3762	1.3333	10.0429	FALSE
GT3	LRPPI3	-6.8762	1.8333	10.5429	FALSE
GT3	LRPPI4	-5.3762	3.3333	12.0429	FALSE
GT3	ME1	-6.2095	2.5000	11.2095	FALSE
LRPPI1	LRPPI2	-8.0429	0.6667	9.3762	FALSE
LRPPI1	LRPPI3	-7.5429	1.1667	9.8762	FALSE
LRPPI1	LRPPI4	-6.0429	2.6667	11.3762	FALSE
LRPPI1	ME1	-6.8762	1.8333	10.5429	FALSE
LRPPI2	LRPPI3	-8.2095	0.5000	9.2095	FALSE
LRPPI2	LRPPI4	-6.7095	2.0000	10.7095	FALSE
LRPPI2	ME1	-7.5429	1.1667	9.8762	FALSE
LRPPI3	LRPPI4	-7.2095	1.5000	10.2095	FALSE
LRPPI3	ME1	-8.0429	0.6667	9.3762	FALSE
LRPPI4	ME1	-9.5429	-0.8333	7.8762	FALSE

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