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Ricardo Montañana Gómez
2020-11-20 11:23:40 +01:00
commit 5611e5bc01
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2
data/tanveer/monks-1/conxuntos.dat Executable file
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40 24 21 17 18 46 106 93 60 90 96 23 100 26 49 39 98 32 15 52 50 16 38 59 51 14 20 33 25 31 55 112 13 61 108 117 4 114 69 63 1 104 65 77 101 111 115 73 71 6 62 47 7 70 75 116 72 84 48 0 102 88
45 91 58 37 10 95 103 53 56 57 82 105 54 27 78 22 85 12 94 81 29 11 34 35 86 28 44 19 80 42 43 119 67 41 8 87 110 107 74 66 30 121 79 2 9 97 118 120 3 122 99 109 76 68 83 5 92 89 113 36 64 123

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data/tanveer/monks-1/conxuntos_kfold.dat Executable file
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58 21 106 19 55 60 10 37 40 38 59 18 44 52 29 34 103 14 49 39 90 56 82 20 91 28 78 35 32 98 43 81 33 23 16 93 94 26 53 57 25 85 80 51 50 95 76 101 113 77 74 64 65 88 30 75 72 36 119 73 3 68 2 102 118 89 71 107 6 41 120 1 63 83 13 5 99 69 70 97 104 8 92 47 122 108 61 84 7 67 114 79 110
24 45 12 27 54 96 42 86 31 17 46 22 15 105 100 11 9 121 0 123 115 116 87 109 62 111 48 117 66 112 4
78 10 29 51 46 31 85 43 37 39 57 34 81 100 33 52 49 42 91 55 16 56 20 11 45 18 90 38 54 26 15 35 82 59 40 22 14 103 95 27 50 80 17 106 19 21 114 61 2 107 36 99 118 69 68 73 8 110 76 13 77 104 71 64 74 41 5 9 6 75 116 112 70 65 121 30 113 92 84 101 7 111 122 97 89 79 3 1 115 119 4 120 108
12 93 53 44 60 28 24 86 32 96 98 25 58 94 23 105 87 0 48 109 123 117 102 63 62 72 47 83 88 67 66
32 18 22 17 38 91 50 15 10 80 45 86 49 60 43 78 34 103 51 81 21 37 85 35 39 59 100 40 24 11 106 25 16 28 26 29 98 90 96 94 12 23 58 56 105 53 41 6 5 69 107 112 115 67 79 102 3 2 104 99 87 92 109 64 111 89 36 61 4 48 72 66 101 73 76 88 63 74 71 117 7 116 65 75 114 30 123 9 77 70 84 62 97
19 95 82 46 14 33 27 31 55 54 42 57 44 93 20 52 122 110 1 113 120 68 118 121 83 108 8 119 47 13 0
54 26 44 81 98 28 96 18 24 25 91 50 39 86 33 59 80 105 42 20 95 58 34 12 15 29 43 100 38 32 78 46 90 21 16 40 45 82 49 31 27 57 35 51 55 103 67 13 7 41 109 66 101 72 71 120 65 48 112 79 104 0 9 108 110 61 74 102 8 47 69 73 70 84 99 62 97 77 83 118 121 6 63 1 111 88 36 116 89 3 107 119 115
56 22 14 94 37 17 60 53 93 52 23 10 106 85 19 11 87 114 75 5 76 30 113 2 64 122 92 4 68 117 123

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data/tanveer/monks-1/le_datos.m Executable file
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printf('lendo problema %s ...\n', problema);
n_entradas= 6; n_clases= 2;
n_fich= 2; fich{1}= 'monks-1.train'; n_patrons(1)= 124; fich{2}= 'monks-1.test'; n_patrons(2)= 432;
n_max= max(n_patrons);
x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
n_patrons_total = sum(n_patrons); n_iter=0;
for i_fich=1:n_fich
f=fopen(fich{i_fich}, 'r');
if -1==f
error('erro en fopen abrindo %s\n', fich{i_fich});
end
for i=1:n_patrons(i_fich)
fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
cl(i_fich,i) = fscanf(f,'%i',1); % lectura da clase
for j = 1:n_entradas
x(i_fich,i,j) = fscanf(f,'%i',1);
end
fscanf(f,'%s',1); % le e descarta o ID
end
fclose(f);
end

5
data/tanveer/monks-1/monks-1.cost Executable file
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% Rows Columns
2 2
% Matrix elements
0.0 1.0
1.0 0.0

432
data/tanveer/monks-1/monks-1.test Executable file
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1 1 1 1 1 1 1 data_1
1 1 1 1 1 1 2 data_2
1 1 1 1 1 2 1 data_3
1 1 1 1 1 2 2 data_4
1 1 1 1 1 3 1 data_5
1 1 1 1 1 3 2 data_6
1 1 1 1 1 4 1 data_7
1 1 1 1 1 4 2 data_8
1 1 1 1 2 1 1 data_9
1 1 1 1 2 1 2 data_10
1 1 1 1 2 2 1 data_11
1 1 1 1 2 2 2 data_12
1 1 1 1 2 3 1 data_13
1 1 1 1 2 3 2 data_14
1 1 1 1 2 4 1 data_15
1 1 1 1 2 4 2 data_16
1 1 1 1 3 1 1 data_17
1 1 1 1 3 1 2 data_18
1 1 1 1 3 2 1 data_19
1 1 1 1 3 2 2 data_20
1 1 1 1 3 3 1 data_21
1 1 1 1 3 3 2 data_22
1 1 1 1 3 4 1 data_23
1 1 1 1 3 4 2 data_24
1 1 1 2 1 1 1 data_25
1 1 1 2 1 1 2 data_26
1 1 1 2 1 2 1 data_27
1 1 1 2 1 2 2 data_28
1 1 1 2 1 3 1 data_29
1 1 1 2 1 3 2 data_30
1 1 1 2 1 4 1 data_31
1 1 1 2 1 4 2 data_32
1 1 1 2 2 1 1 data_33
1 1 1 2 2 1 2 data_34
1 1 1 2 2 2 1 data_35
1 1 1 2 2 2 2 data_36
1 1 1 2 2 3 1 data_37
1 1 1 2 2 3 2 data_38
1 1 1 2 2 4 1 data_39
1 1 1 2 2 4 2 data_40
1 1 1 2 3 1 1 data_41
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1 1 1 2 3 2 2 data_44
1 1 1 2 3 3 1 data_45
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1 1 1 2 3 4 1 data_47
1 1 1 2 3 4 2 data_48
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0 1 2 1 1 3 2 data_54
0 1 2 1 1 4 1 data_55
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1 1 2 1 2 1 1 data_57
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1 1 2 1 3 1 1 data_65
1 1 2 1 3 1 2 data_66
0 1 2 1 3 2 1 data_67
0 1 2 1 3 2 2 data_68
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0 1 2 1 3 3 2 data_70
0 1 2 1 3 4 1 data_71
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1 1 2 2 1 1 1 data_73
1 1 2 2 1 1 2 data_74
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0 1 2 2 1 2 2 data_76
0 1 2 2 1 3 1 data_77
0 1 2 2 1 3 2 data_78
0 1 2 2 1 4 1 data_79
0 1 2 2 1 4 2 data_80
1 1 2 2 2 1 1 data_81
1 1 2 2 2 1 2 data_82
0 1 2 2 2 2 1 data_83
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0 1 2 2 2 3 2 data_86
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0 1 2 2 2 4 2 data_88
1 1 2 2 3 1 1 data_89
1 1 2 2 3 1 2 data_90
0 1 2 2 3 2 1 data_91
0 1 2 2 3 2 2 data_92
0 1 2 2 3 3 1 data_93
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0 1 2 2 3 4 1 data_95
0 1 2 2 3 4 2 data_96
1 1 3 1 1 1 1 data_97
1 1 3 1 1 1 2 data_98
0 1 3 1 1 2 1 data_99
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0 1 3 1 1 3 1 data_101
0 1 3 1 1 3 2 data_102
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0 1 3 1 1 4 2 data_104
1 1 3 1 2 1 1 data_105
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0 1 3 1 2 3 1 data_109
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1 1 3 1 3 1 1 data_113
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1 3 1 2 1 1 1 data_313
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1 3 3 1 1 1 1 data_385
1 3 3 1 1 1 2 data_386
1 3 3 1 1 2 1 data_387
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1 3 3 1 2 1 1 data_393
1 3 3 1 2 1 2 data_394
1 3 3 1 2 2 1 data_395
1 3 3 1 2 2 2 data_396
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1 3 3 1 2 3 2 data_398
1 3 3 1 2 4 1 data_399
1 3 3 1 2 4 2 data_400
1 3 3 1 3 1 1 data_401
1 3 3 1 3 1 2 data_402
1 3 3 1 3 2 1 data_403
1 3 3 1 3 2 2 data_404
1 3 3 1 3 3 1 data_405
1 3 3 1 3 3 2 data_406
1 3 3 1 3 4 1 data_407
1 3 3 1 3 4 2 data_408
1 3 3 2 1 1 1 data_409
1 3 3 2 1 1 2 data_410
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1 3 3 2 1 4 1 data_415
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1 3 3 2 2 1 1 data_417
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1 3 3 2 3 2 1 data_427
1 3 3 2 3 2 2 data_428
1 3 3 2 3 3 1 data_429
1 3 3 2 3 3 2 data_430
1 3 3 2 3 4 1 data_431
1 3 3 2 3 4 2 data_432

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data/tanveer/monks-1/monks-1.train Executable file
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1 1 1 1 1 3 1 data_5
1 1 1 1 1 3 2 data_6
1 1 1 1 3 2 1 data_19
1 1 1 1 3 3 2 data_22
1 1 1 2 1 2 1 data_27
1 1 1 2 1 2 2 data_28
1 1 1 2 2 3 1 data_37
1 1 1 2 2 4 1 data_39
1 1 1 2 3 1 2 data_42
1 1 2 1 1 1 2 data_50
0 1 2 1 1 2 1 data_51
0 1 2 1 1 3 1 data_53
0 1 2 1 1 4 2 data_56
1 1 2 1 2 1 1 data_57
0 1 2 1 2 3 1 data_61
0 1 2 1 2 3 2 data_62
0 1 2 1 2 4 2 data_64
0 1 2 1 3 2 1 data_67
0 1 2 1 3 4 2 data_72
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0 1 2 2 2 4 1 data_87
0 1 2 2 2 4 2 data_88
0 1 2 2 3 2 2 data_92
0 1 2 2 3 3 1 data_93
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1 3 1 1 1 1 1 data_289
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1 3 1 1 2 1 1 data_297
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1 3 1 2 1 1 1 data_313
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1 3 2 1 1 1 1 data_337
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1 3 2 2 1 1 1 data_361
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1 3 2 2 3 1 1 data_377
0 3 2 2 3 2 1 data_379
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1 3 3 1 1 1 1 data_385
1 3 3 1 1 2 1 data_387
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1 3 3 2 1 1 1 data_409
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1 3 3 2 3 1 2 data_426
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1 3 3 2 3 3 2 data_430
1 3 3 2 3 4 2 data_432

10
data/tanveer/monks-1/monks-1.txt Executable file
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n_entradas= 6
n_clases= 2
n_arquivos= 2
fich1= monks-1_train_R.dat
n_patrons1= 124
fich2= monks-1_test_R.dat
n_patrons2= 432
n_patrons_entrena= 62
n_patrons_valida= 62
n_conxuntos= 1

441
data/tanveer/monks-1/monks-1_test.arff Executable file
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@relation monks-1
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'clase' {0,1}
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133
data/tanveer/monks-1/monks-1_train.arff Executable file
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@@ -0,0 +1,133 @@
@relation monks-1
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'clase' {0,1}
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125
data/tanveer/monks-1/monks-1_train_R.dat Executable file
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43 0.45421 2.29767 2.18919 2.39467 0.970018 0.740391 0
44 0.45421 2.29767 2.18919 2.39467 2.50219 0.740391 0
45 0.45421 2.29767 2.18919 2.39467 2.50219 2.07309 0
46 1.47618 0.381467 0.800453 0.418553 1.7361 0.740391 0
47 1.47618 0.381467 0.800453 0.418553 1.7361 2.07309 0
48 1.47618 0.381467 0.800453 1.40661 0.203934 0.740391 1
49 1.47618 0.381467 0.800453 1.40661 0.203934 2.07309 1
50 1.47618 0.381467 0.800453 1.40661 0.970018 2.07309 0
51 1.47618 0.381467 0.800453 1.40661 1.7361 0.740391 0
52 1.47618 0.381467 0.800453 1.40661 2.50219 0.740391 0
53 1.47618 0.381467 0.800453 1.40661 2.50219 2.07309 0
54 1.47618 0.381467 0.800453 2.39467 2.50219 0.740391 0
55 1.47618 0.381467 2.18919 0.418553 0.970018 2.07309 0
56 1.47618 0.381467 2.18919 0.418553 1.7361 0.740391 0
57 1.47618 0.381467 2.18919 0.418553 2.50219 2.07309 0
58 1.47618 0.381467 2.18919 1.40661 1.7361 0.740391 0
59 1.47618 0.381467 2.18919 1.40661 2.50219 2.07309 0
60 1.47618 0.381467 2.18919 2.39467 0.970018 2.07309 0
61 1.47618 0.381467 2.18919 2.39467 2.50219 0.740391 0
62 1.47618 1.33957 0.800453 0.418553 0.970018 0.740391 1
63 1.47618 1.33957 0.800453 0.418553 0.970018 2.07309 1
64 1.47618 1.33957 0.800453 0.418553 1.7361 0.740391 1
65 1.47618 1.33957 0.800453 1.40661 1.7361 2.07309 1
66 1.47618 1.33957 0.800453 2.39467 0.203934 0.740391 1
67 1.47618 1.33957 0.800453 2.39467 0.203934 2.07309 1
68 1.47618 1.33957 0.800453 2.39467 0.970018 2.07309 1
69 1.47618 1.33957 0.800453 2.39467 1.7361 2.07309 1
70 1.47618 1.33957 0.800453 2.39467 2.50219 2.07309 1
71 1.47618 1.33957 2.18919 0.418553 0.203934 0.740391 1
72 1.47618 1.33957 2.18919 0.418553 1.7361 2.07309 1
73 1.47618 1.33957 2.18919 0.418553 2.50219 0.740391 1
74 1.47618 1.33957 2.18919 0.418553 2.50219 2.07309 1
75 1.47618 1.33957 2.18919 1.40661 0.970018 0.740391 1
76 1.47618 1.33957 2.18919 2.39467 2.50219 0.740391 1
77 1.47618 2.29767 0.800453 0.418553 0.203934 0.740391 1
78 1.47618 2.29767 0.800453 1.40661 0.203934 0.740391 1
79 1.47618 2.29767 0.800453 1.40661 1.7361 0.740391 0
80 1.47618 2.29767 0.800453 2.39467 0.203934 2.07309 1
81 1.47618 2.29767 0.800453 2.39467 1.7361 0.740391 0
82 1.47618 2.29767 0.800453 2.39467 2.50219 2.07309 0
83 1.47618 2.29767 2.18919 0.418553 1.7361 2.07309 0
84 1.47618 2.29767 2.18919 1.40661 0.203934 0.740391 1
85 1.47618 2.29767 2.18919 1.40661 0.203934 2.07309 1
86 1.47618 2.29767 2.18919 1.40661 0.970018 0.740391 0
87 1.47618 2.29767 2.18919 2.39467 1.7361 2.07309 0
88 2.49815 0.381467 0.800453 0.418553 0.203934 0.740391 1
89 2.49815 0.381467 0.800453 0.418553 0.203934 2.07309 1
90 2.49815 0.381467 0.800453 1.40661 0.203934 0.740391 1
91 2.49815 0.381467 0.800453 1.40661 0.970018 2.07309 0
92 2.49815 0.381467 0.800453 2.39467 0.970018 2.07309 0
93 2.49815 0.381467 2.18919 0.418553 0.203934 0.740391 1
94 2.49815 0.381467 2.18919 0.418553 0.970018 2.07309 0
95 2.49815 0.381467 2.18919 1.40661 0.970018 2.07309 0
96 2.49815 0.381467 2.18919 1.40661 1.7361 2.07309 0
97 2.49815 0.381467 2.18919 2.39467 0.970018 2.07309 0
98 2.49815 1.33957 0.800453 0.418553 0.203934 0.740391 1
99 2.49815 1.33957 0.800453 0.418553 2.50219 2.07309 0
100 2.49815 1.33957 0.800453 1.40661 0.203934 2.07309 1
101 2.49815 1.33957 0.800453 1.40661 2.50219 2.07309 0
102 2.49815 1.33957 2.18919 0.418553 0.203934 0.740391 1
103 2.49815 1.33957 2.18919 0.418553 0.203934 2.07309 1
104 2.49815 1.33957 2.18919 0.418553 1.7361 2.07309 0
105 2.49815 1.33957 2.18919 2.39467 0.203934 0.740391 1
106 2.49815 1.33957 2.18919 2.39467 0.970018 0.740391 0
107 2.49815 1.33957 2.18919 2.39467 2.50219 0.740391 0
108 2.49815 2.29767 0.800453 0.418553 0.203934 0.740391 1
109 2.49815 2.29767 0.800453 0.418553 0.970018 0.740391 1
110 2.49815 2.29767 0.800453 0.418553 2.50219 2.07309 1
111 2.49815 2.29767 0.800453 1.40661 1.7361 2.07309 1
112 2.49815 2.29767 0.800453 1.40661 2.50219 2.07309 1
113 2.49815 2.29767 0.800453 2.39467 0.203934 2.07309 1
114 2.49815 2.29767 0.800453 2.39467 0.970018 0.740391 1
115 2.49815 2.29767 0.800453 2.39467 0.970018 2.07309 1
116 2.49815 2.29767 0.800453 2.39467 2.50219 2.07309 1
117 2.49815 2.29767 2.18919 0.418553 0.203934 0.740391 1
118 2.49815 2.29767 2.18919 0.418553 1.7361 2.07309 1
119 2.49815 2.29767 2.18919 0.418553 2.50219 0.740391 1
120 2.49815 2.29767 2.18919 0.418553 2.50219 2.07309 1
121 2.49815 2.29767 2.18919 2.39467 0.203934 2.07309 1
122 2.49815 2.29767 2.18919 2.39467 0.970018 2.07309 1
123 2.49815 2.29767 2.18919 2.39467 1.7361 2.07309 1
124 2.49815 2.29767 2.18919 2.39467 2.50219 2.07309 1

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1. Title: The Monk's Problems
2. Sources:
(a) Donor: Sebastian Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA
E-mail: thrun@cs.cmu.edu
(b) Date: October 1992
3. Past Usage:
- See File: thrun.comparison.ps.Z
- Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation,
School of Information Technology and Engineering, Reports of Machine
Learning and Inference Laboratory, MLI 93-2, Center for Artificial
Intelligence, George Mason University, March 1993.
- Wnek, J. and Michalski, R.S., "Comparing Symbolic and
Subsymbolic Learning: Three Studies," in Machine Learning: A
Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.),
Morgan Kaufmann, San Mateo, CA, 1993.
4. Relevant Information:
The MONK's problem were the basis of a first international comparison
of learning algorithms. The result of this comparison is summarized in
"The MONK's Problems - A Performance Comparison of Different Learning
algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I. Bratko, B.
Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S.E. Fahlman, D. Fisher,
R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J. Kreuziger, R.S.
Michalski, T. Mitchell, P. Pachowicz, Y. Reich H. Vafaie, W. Van de
Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
1991.
One significant characteristic of this comparison is that it was
performed by a collection of researchers, each of whom was an advocate
of the technique they tested (often they were the creators of the
various methods). In this sense, the results are less biased than in
comparisons performed by a single person advocating a specific
learning method, and more accurately reflect the generalization
behavior of the learning techniques as applied by knowledgeable users.
There are three MONK's problems. The domains for all MONK's problems
are the same (described below). One of the MONK's problems has noise
added. For each problem, the domain has been partitioned into a train
and test set.
5. Number of Instances: 432
6. Number of Attributes: 8 (including class attribute)
7. Attribute information:
1. class: 0, 1
2. a1: 1, 2, 3
3. a2: 1, 2, 3
4. a3: 1, 2
5. a4: 1, 2, 3
6. a5: 1, 2, 3, 4
7. a6: 1, 2
8. Id: (A unique symbol for each instance)
8. Missing Attribute Values: None
9. Target Concepts associated to the MONK's problem:
MONK-1: (a1 = a2) or (a5 = 1)
MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
(5% class noise added to the training set)