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

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

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data/tanveer/monks-3/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-3.train'; n_patrons(1)= 122; fich{2}= 'monks-3.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-3/monks-3.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-3/monks-3.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
0 1 1 1 1 4 1 data_7
0 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
0 1 1 1 2 4 1 data_15
0 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
0 1 1 1 3 4 1 data_23
0 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
0 1 1 2 1 4 1 data_31
0 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
0 1 1 2 2 4 1 data_39
0 1 1 2 2 4 2 data_40
1 1 1 2 3 1 1 data_41
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0 1 1 2 3 4 2 data_48
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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 2 2 2 data_60
1 1 2 1 2 3 1 data_61
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0 1 2 1 2 4 1 data_63
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1 1 2 1 3 1 1 data_65
1 1 2 1 3 1 2 data_66
1 1 2 1 3 2 1 data_67
1 1 2 1 3 2 2 data_68
1 1 2 1 3 3 1 data_69
1 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 2 data_74
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1 1 2 2 1 2 2 data_76
1 1 2 2 1 3 1 data_77
1 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
1 1 2 2 2 2 1 data_83
1 1 2 2 2 2 2 data_84
1 1 2 2 2 3 1 data_85
1 1 2 2 2 3 2 data_86
0 1 2 2 2 4 1 data_87
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
1 1 2 2 3 2 1 data_91
1 1 2 2 3 2 2 data_92
1 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
0 1 3 1 1 1 1 data_97
0 1 3 1 1 1 2 data_98
0 1 3 1 1 2 1 data_99
0 1 3 1 1 2 2 data_100
1 1 3 1 1 3 1 data_101
1 1 3 1 1 3 2 data_102
0 1 3 1 1 4 1 data_103
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0 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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0 1 3 1 2 4 1 data_111
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0 1 3 1 3 1 1 data_113
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0 1 3 1 3 2 1 data_115
0 1 3 1 3 2 2 data_116
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0 1 3 1 3 3 2 data_118
0 1 3 1 3 4 1 data_119
0 1 3 1 3 4 2 data_120
0 1 3 2 1 1 1 data_121
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1 2 1 1 3 2 1 data_163
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1 2 1 2 2 3 2 data_182
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1 2 2 1 1 1 1 data_193
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1 2 2 1 3 3 2 data_214
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1 3 1 2 1 1 1 data_313
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1 3 1 2 1 2 1 data_315
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0 3 3 1 1 1 2 data_386
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0 3 3 1 3 1 2 data_402
0 3 3 1 3 2 1 data_403
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0 3 3 1 3 3 1 data_405
0 3 3 1 3 3 2 data_406
0 3 3 1 3 4 1 data_407
0 3 3 1 3 4 2 data_408
0 3 3 2 1 1 1 data_409
0 3 3 2 1 1 2 data_410
0 3 3 2 1 2 1 data_411
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0 3 3 2 1 4 1 data_415
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0 3 3 2 2 1 1 data_417
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0 3 3 2 3 2 2 data_428
0 3 3 2 3 3 1 data_429
0 3 3 2 3 3 2 data_430
0 3 3 2 3 4 1 data_431
0 3 3 2 3 4 2 data_432

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data/tanveer/monks-3/monks-3.train Executable file
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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
0 1 1 1 1 3 1 data_5
0 1 1 1 1 4 1 data_7
1 1 1 1 2 1 1 data_9
1 1 1 1 2 2 2 data_12
0 1 1 1 2 4 2 data_16
1 1 1 2 1 2 2 data_28
0 1 1 2 1 4 2 data_32
1 1 1 2 2 2 2 data_36
0 1 1 2 2 4 1 data_39
0 1 1 2 2 4 2 data_40
1 1 1 2 3 1 1 data_41
1 1 1 2 3 1 2 data_42
1 1 1 2 3 3 1 data_45
1 1 1 2 3 3 2 data_46
1 1 2 1 1 3 1 data_53
1 1 2 1 2 2 1 data_59
1 1 2 1 2 2 2 data_60
0 1 2 1 2 3 1 data_61
1 1 2 1 3 1 1 data_65
1 1 2 1 3 1 2 data_66
1 1 2 1 3 2 1 data_67
1 1 2 1 3 2 2 data_68
1 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 2 1 1 data_81
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0 1 3 2 3 4 1 data_143
1 2 1 1 1 1 1 data_145
1 2 1 1 1 1 2 data_146
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0 2 3 2 3 1 1 data_281
0 2 3 2 3 2 1 data_283
0 2 3 2 3 4 2 data_288
1 3 1 1 1 1 1 data_289
1 3 1 1 1 2 1 data_291
1 3 1 1 1 3 1 data_293
0 3 1 1 2 4 2 data_304
1 3 1 1 3 1 2 data_306
0 3 1 1 3 4 2 data_312
1 3 1 2 1 2 1 data_315
1 3 1 2 2 3 2 data_326
0 3 1 2 2 4 2 data_328
1 3 1 2 3 1 1 data_329
1 3 2 1 1 2 2 data_340
0 3 2 1 1 4 1 data_343
1 3 2 1 2 3 1 data_349
1 3 2 1 3 1 2 data_354
1 3 2 2 1 2 2 data_364
1 3 2 2 1 3 2 data_366
1 3 2 2 2 1 2 data_370
1 3 2 2 3 1 1 data_377
1 3 2 2 3 3 2 data_382
0 3 2 2 3 4 1 data_383
1 3 3 1 1 3 2 data_390
1 3 3 1 1 4 1 data_391
0 3 3 1 2 4 2 data_400
0 3 3 1 3 1 1 data_401
0 3 3 1 3 2 1 data_403
0 3 3 1 3 2 2 data_404
0 3 3 1 3 4 1 data_407
0 3 3 2 1 1 1 data_409
0 3 3 2 1 1 2 data_410
0 3 3 2 2 2 2 data_420
0 3 3 2 2 3 2 data_422
0 3 3 2 3 1 1 data_425
0 3 3 2 3 3 2 data_430
0 3 3 2 3 4 2 data_432

10
data/tanveer/monks-3/monks-3.txt Executable file
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n_entradas= 6
n_clases= 2
n_arquivos= 2
fich1= monks-3_train_R.dat
n_patrons1= 122
fich2= monks-3_test_R.dat
n_patrons2= 432
n_patrons_entrena= 61
n_patrons_valida= 61
n_conxuntos= 1

441
data/tanveer/monks-3/monks-3_test.arff Executable file
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@relation monks-3
@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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131
data/tanveer/monks-3/monks-3_train.arff Executable file
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@relation monks-3
@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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123
data/tanveer/monks-3/monks-3_train_R.dat Executable file
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43 0.490585 2.41681 2.22531 0.418876 0.237339 2.14334 0
44 0.490585 2.41681 2.22531 0.418876 1.02604 0.779683 0
45 0.490585 2.41681 2.22531 0.418876 2.60343 2.14334 0
46 0.490585 2.41681 2.22531 1.40232 1.81473 2.14334 0
47 0.490585 2.41681 2.22531 1.40232 2.60343 2.14334 0
48 0.490585 2.41681 2.22531 2.38577 2.60343 0.779683 0
49 1.53976 0.428121 0.821904 0.418876 0.237339 0.779683 1
50 1.53976 0.428121 0.821904 0.418876 0.237339 2.14334 1
51 1.53976 0.428121 0.821904 0.418876 2.60343 0.779683 0
52 1.53976 0.428121 0.821904 0.418876 2.60343 2.14334 0
53 1.53976 0.428121 0.821904 1.40232 0.237339 0.779683 1
54 1.53976 0.428121 0.821904 1.40232 0.237339 2.14334 1
55 1.53976 0.428121 0.821904 2.38577 1.02604 2.14334 1
56 1.53976 0.428121 0.821904 2.38577 1.81473 2.14334 1
57 1.53976 0.428121 0.821904 2.38577 2.60343 0.779683 0
58 1.53976 0.428121 2.22531 0.418876 1.02604 2.14334 1
59 1.53976 0.428121 2.22531 1.40232 2.60343 0.779683 0
60 1.53976 0.428121 2.22531 2.38577 0.237339 2.14334 1
61 1.53976 1.42247 0.821904 0.418876 1.81473 2.14334 1
62 1.53976 1.42247 0.821904 0.418876 2.60343 2.14334 0
63 1.53976 1.42247 0.821904 1.40232 0.237339 2.14334 1
64 1.53976 1.42247 0.821904 1.40232 1.02604 0.779683 0
65 1.53976 1.42247 0.821904 2.38577 0.237339 0.779683 1
66 1.53976 1.42247 0.821904 2.38577 1.02604 2.14334 1
67 1.53976 1.42247 0.821904 2.38577 1.81473 0.779683 0
68 1.53976 1.42247 0.821904 2.38577 1.81473 2.14334 0
69 1.53976 1.42247 0.821904 2.38577 2.60343 2.14334 0
70 1.53976 1.42247 2.22531 0.418876 1.02604 2.14334 1
71 1.53976 1.42247 2.22531 1.40232 0.237339 2.14334 1
72 1.53976 1.42247 2.22531 1.40232 1.81473 0.779683 1
73 1.53976 1.42247 2.22531 1.40232 1.81473 2.14334 1
74 1.53976 1.42247 2.22531 2.38577 2.60343 0.779683 0
75 1.53976 2.41681 0.821904 0.418876 1.81473 0.779683 1
76 1.53976 2.41681 0.821904 1.40232 0.237339 0.779683 0
77 1.53976 2.41681 0.821904 1.40232 1.02604 0.779683 0
78 1.53976 2.41681 0.821904 1.40232 1.02604 2.14334 0
79 1.53976 2.41681 0.821904 1.40232 1.81473 2.14334 0
80 1.53976 2.41681 0.821904 2.38577 1.81473 0.779683 0
81 1.53976 2.41681 2.22531 0.418876 0.237339 2.14334 0
82 1.53976 2.41681 2.22531 0.418876 1.02604 2.14334 0
83 1.53976 2.41681 2.22531 0.418876 2.60343 0.779683 0
84 1.53976 2.41681 2.22531 1.40232 1.81473 0.779683 0
85 1.53976 2.41681 2.22531 1.40232 2.60343 2.14334 0
86 1.53976 2.41681 2.22531 2.38577 0.237339 0.779683 0
87 1.53976 2.41681 2.22531 2.38577 1.02604 0.779683 0
88 1.53976 2.41681 2.22531 2.38577 2.60343 2.14334 0
89 2.58893 0.428121 0.821904 0.418876 0.237339 0.779683 1
90 2.58893 0.428121 0.821904 0.418876 1.02604 0.779683 1
91 2.58893 0.428121 0.821904 0.418876 1.81473 0.779683 1
92 2.58893 0.428121 0.821904 1.40232 2.60343 2.14334 0
93 2.58893 0.428121 0.821904 2.38577 0.237339 2.14334 1
94 2.58893 0.428121 0.821904 2.38577 2.60343 2.14334 0
95 2.58893 0.428121 2.22531 0.418876 1.02604 0.779683 1
96 2.58893 0.428121 2.22531 1.40232 1.81473 2.14334 1
97 2.58893 0.428121 2.22531 1.40232 2.60343 2.14334 0
98 2.58893 0.428121 2.22531 2.38577 0.237339 0.779683 1
99 2.58893 1.42247 0.821904 0.418876 1.02604 2.14334 1
100 2.58893 1.42247 0.821904 0.418876 2.60343 0.779683 0
101 2.58893 1.42247 0.821904 1.40232 1.81473 0.779683 1
102 2.58893 1.42247 0.821904 2.38577 0.237339 2.14334 1
103 2.58893 1.42247 2.22531 0.418876 1.02604 2.14334 1
104 2.58893 1.42247 2.22531 0.418876 1.81473 2.14334 1
105 2.58893 1.42247 2.22531 1.40232 0.237339 2.14334 1
106 2.58893 1.42247 2.22531 2.38577 0.237339 0.779683 1
107 2.58893 1.42247 2.22531 2.38577 1.81473 2.14334 1
108 2.58893 1.42247 2.22531 2.38577 2.60343 0.779683 0
109 2.58893 2.41681 0.821904 0.418876 1.81473 2.14334 1
110 2.58893 2.41681 0.821904 0.418876 2.60343 0.779683 1
111 2.58893 2.41681 0.821904 1.40232 2.60343 2.14334 0
112 2.58893 2.41681 0.821904 2.38577 0.237339 0.779683 0
113 2.58893 2.41681 0.821904 2.38577 1.02604 0.779683 0
114 2.58893 2.41681 0.821904 2.38577 1.02604 2.14334 0
115 2.58893 2.41681 0.821904 2.38577 2.60343 0.779683 0
116 2.58893 2.41681 2.22531 0.418876 0.237339 0.779683 0
117 2.58893 2.41681 2.22531 0.418876 0.237339 2.14334 0
118 2.58893 2.41681 2.22531 1.40232 1.02604 2.14334 0
119 2.58893 2.41681 2.22531 1.40232 1.81473 2.14334 0
120 2.58893 2.41681 2.22531 2.38577 0.237339 0.779683 0
121 2.58893 2.41681 2.22531 2.38577 1.81473 2.14334 0
122 2.58893 2.41681 2.22531 2.38577 2.60343 2.14334 0

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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)