nuswide_BoW

mldr.datasets::get.mldr("nuswide_BoW")

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Full dataset

MULAN/MEKA (.arff) MULAN (.xml) LibSVM KEEL mldr

Partitions: select your desired partitioning strategy, validation and format

	Random	Stratified	Iterative stratified
Hold out	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr
10-fold cross validation	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr	MULAN MEKA LibSVM KEEL mldr

Summary

Instances	269648
Attributes	582
Inputs	501
Labels	81
Labelsets	18430
Single labelsets	11378
Max frequency	60301
Cardinality	1.8685
Density	0.0231
Mean IR	95.1187
SCUMBLE	0.171
TCS	20.4328

Citation

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Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.

Attributes

all attributes labels

string image_name numeric BoW_l2_0 numeric BoW_l2_1 numeric BoW_l2_2 numeric BoW_l2_3 numeric BoW_l2_4 numeric BoW_l2_5 numeric BoW_l2_6 numeric BoW_l2_7 numeric BoW_l2_8 numeric BoW_l2_9 numeric BoW_l2_10 numeric BoW_l2_11 numeric BoW_l2_12 numeric BoW_l2_13 numeric BoW_l2_14 numeric BoW_l2_15 numeric BoW_l2_16 numeric BoW_l2_17 numeric BoW_l2_18 numeric BoW_l2_19 numeric BoW_l2_20 numeric BoW_l2_21 numeric BoW_l2_22 numeric BoW_l2_23 numeric BoW_l2_24 numeric BoW_l2_25 numeric BoW_l2_26 numeric BoW_l2_27 numeric BoW_l2_28 numeric BoW_l2_29 numeric BoW_l2_30 numeric BoW_l2_31 numeric BoW_l2_32 numeric BoW_l2_33 numeric BoW_l2_34 numeric BoW_l2_35 numeric BoW_l2_36 numeric BoW_l2_37 numeric BoW_l2_38 numeric BoW_l2_39 numeric BoW_l2_40 numeric BoW_l2_41 numeric BoW_l2_42 numeric BoW_l2_43 numeric BoW_l2_44 numeric BoW_l2_45 numeric BoW_l2_46 numeric BoW_l2_47 numeric BoW_l2_48 numeric BoW_l2_49 numeric BoW_l2_50 numeric BoW_l2_51 numeric BoW_l2_52 numeric BoW_l2_53 numeric BoW_l2_54 numeric BoW_l2_55 numeric BoW_l2_56 numeric BoW_l2_57 numeric BoW_l2_58 numeric BoW_l2_59 numeric BoW_l2_60 numeric BoW_l2_61 numeric BoW_l2_62 numeric BoW_l2_63 numeric BoW_l2_64 numeric BoW_l2_65 numeric BoW_l2_66 numeric BoW_l2_67 numeric BoW_l2_68 numeric BoW_l2_69 numeric BoW_l2_70 numeric BoW_l2_71 numeric BoW_l2_72 numeric BoW_l2_73 numeric BoW_l2_74 numeric BoW_l2_75 numeric BoW_l2_76 numeric BoW_l2_77 numeric BoW_l2_78 numeric BoW_l2_79 numeric BoW_l2_80 numeric BoW_l2_81 numeric BoW_l2_82 numeric BoW_l2_83 numeric BoW_l2_84 numeric BoW_l2_85 numeric BoW_l2_86 numeric BoW_l2_87 numeric BoW_l2_88 numeric BoW_l2_89 numeric BoW_l2_90 numeric BoW_l2_91 numeric BoW_l2_92 numeric BoW_l2_93 numeric BoW_l2_94 numeric BoW_l2_95 numeric BoW_l2_96 numeric BoW_l2_97 numeric BoW_l2_98 numeric BoW_l2_99 numeric BoW_l2_100 numeric BoW_l2_101 numeric BoW_l2_102 numeric BoW_l2_103 numeric BoW_l2_104 numeric BoW_l2_105 numeric BoW_l2_106 numeric BoW_l2_107 numeric BoW_l2_108 numeric BoW_l2_109 numeric BoW_l2_110 numeric BoW_l2_111 numeric BoW_l2_112 numeric BoW_l2_113 numeric BoW_l2_114 numeric BoW_l2_115 numeric BoW_l2_116 numeric BoW_l2_117 numeric BoW_l2_118 numeric BoW_l2_119 numeric BoW_l2_120 numeric BoW_l2_121 numeric BoW_l2_122 numeric BoW_l2_123 numeric BoW_l2_124 numeric BoW_l2_125 numeric BoW_l2_126 numeric BoW_l2_127 numeric BoW_l2_128 numeric BoW_l2_129 numeric BoW_l2_130 numeric BoW_l2_131 numeric BoW_l2_132 numeric BoW_l2_133 numeric BoW_l2_134 numeric BoW_l2_135 numeric BoW_l2_136 numeric BoW_l2_137 numeric BoW_l2_138 numeric BoW_l2_139 numeric BoW_l2_140 numeric BoW_l2_141 numeric BoW_l2_142 numeric BoW_l2_143 numeric BoW_l2_144 numeric BoW_l2_145 numeric BoW_l2_146 numeric BoW_l2_147 numeric BoW_l2_148 numeric BoW_l2_149 numeric BoW_l2_150 numeric BoW_l2_151 numeric BoW_l2_152 numeric BoW_l2_153 numeric BoW_l2_154 numeric BoW_l2_155 numeric BoW_l2_156 numeric BoW_l2_157 numeric BoW_l2_158 numeric BoW_l2_159 numeric BoW_l2_160 numeric BoW_l2_161 numeric BoW_l2_162 numeric BoW_l2_163 numeric BoW_l2_164 numeric BoW_l2_165 numeric BoW_l2_166 numeric BoW_l2_167 numeric BoW_l2_168 numeric BoW_l2_169 numeric BoW_l2_170 numeric BoW_l2_171 numeric BoW_l2_172 numeric BoW_l2_173 numeric BoW_l2_174 numeric BoW_l2_175 numeric BoW_l2_176 numeric BoW_l2_177 numeric BoW_l2_178 numeric BoW_l2_179 numeric BoW_l2_180 numeric BoW_l2_181 numeric BoW_l2_182 numeric BoW_l2_183 numeric BoW_l2_184 numeric BoW_l2_185 numeric BoW_l2_186 numeric BoW_l2_187 numeric BoW_l2_188 numeric BoW_l2_189 numeric BoW_l2_190 numeric BoW_l2_191 numeric BoW_l2_192 numeric BoW_l2_193 numeric BoW_l2_194 numeric BoW_l2_195 numeric BoW_l2_196 numeric BoW_l2_197 numeric BoW_l2_198

(382 more attributes)

airport SCUMBLE 0.6004 animal SCUMBLE 0.295 beach SCUMBLE 0.3841 bear SCUMBLE 0.5774 birds SCUMBLE 0.4115 boats SCUMBLE 0.4198 book SCUMBLE 0.0953 bridge SCUMBLE 0.4614 buildings SCUMBLE 0.3078 cars SCUMBLE 0.3881 castle SCUMBLE 0.7384 cat SCUMBLE 0.4742 cityscape SCUMBLE 0.484 clouds SCUMBLE 0.3367 computer SCUMBLE 0.2562 coral SCUMBLE 0.4825 cow SCUMBLE 0.7243 dancing SCUMBLE 0.5835 dog SCUMBLE 0.4778 earthquake SCUMBLE 0.7496 elk SCUMBLE 0.698 fire SCUMBLE 0.4531 fish SCUMBLE 0.5283 flags SCUMBLE 0.5568 flowers SCUMBLE 0.1073 food SCUMBLE 0.0532 fox SCUMBLE 0.6671 frost SCUMBLE 0.4427 garden SCUMBLE 0.3572 glacier SCUMBLE 0.6661 grass SCUMBLE 0.283 harbor SCUMBLE 0.7256 horses SCUMBLE 0.5787 house SCUMBLE 0.3921 lake SCUMBLE 0.3301 leaf SCUMBLE 0.3195 map SCUMBLE 0.0563 military SCUMBLE 0.4432 moon SCUMBLE 0.6046 mountain SCUMBLE 0.408 nighttime SCUMBLE 0.3554 ocean SCUMBLE 0.3477 person SCUMBLE 0.1509 plane SCUMBLE 0.4979 plants SCUMBLE 0.194 police SCUMBLE 0.5156 protest SCUMBLE 0.6314 railroad SCUMBLE 0.4976 rainbow SCUMBLE 0.8136 reflection SCUMBLE 0.3343 road SCUMBLE 0.2748 rocks SCUMBLE 0.3488 running SCUMBLE 0.7108 sand SCUMBLE 0.4816 sign SCUMBLE 0.2808 sky SCUMBLE 0.3399 snow SCUMBLE 0.3515 soccer SCUMBLE 0.8372 sports SCUMBLE 0.5684 statue SCUMBLE 0.3476 street SCUMBLE 0.3884 sun SCUMBLE 0.4439 sunset SCUMBLE 0.3762 surf SCUMBLE 0.8642 swimmers SCUMBLE 0.7604 tattoo SCUMBLE 0.5772 temple SCUMBLE 0.4404 tiger SCUMBLE 0.7127 tower SCUMBLE 0.4987 town SCUMBLE 0.4269 toy SCUMBLE 0.1164 train SCUMBLE 0.4876 tree SCUMBLE 0.3176 valley SCUMBLE 0.4242 vehicle SCUMBLE 0.3075 water SCUMBLE 0.3241 waterfall SCUMBLE 0.6423 wedding SCUMBLE 0.494 whales SCUMBLE 0.7914 window SCUMBLE 0.2209 zebra SCUMBLE 0.7911

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