Data
GTSRB-HueHist

GTSRB-HueHist

active ARFF The data is free to use (when citing the publication). Visibility: public Uploaded 24-07-2019 by Matthias Feurer
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The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. # Pre-calculated features To allow scientists without a background in image processing to participate, we several provide pre-calculated feature sets. Each feature set contains the same directory structure as the training image set. For details on the parameters of the feature algorithm, please have a look at the file Feature_description.txt which is part of each archive file. \# Hue Histograms For each image in the training set, the file contains a 256-bin histogram of hue values (HSV color space).

257 features

class (target)nominal43 unique values
0 missing
pixel-00000numeric2817 unique values
0 missing
pixel-00001numeric8639 unique values
0 missing
pixel-00002numeric9637 unique values
0 missing
pixel-00003numeric9817 unique values
0 missing
pixel-00004numeric9783 unique values
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pixel-00005numeric9460 unique values
0 missing
pixel-00006numeric9183 unique values
0 missing
pixel-00007numeric9087 unique values
0 missing
pixel-00008numeric8285 unique values
0 missing
pixel-00009numeric9456 unique values
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pixel-00010numeric7601 unique values
0 missing
pixel-00011numeric8972 unique values
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pixel-00012numeric8015 unique values
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pixel-00013numeric7754 unique values
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pixel-00014numeric8928 unique values
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pixel-00015numeric7297 unique values
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pixel-00016numeric7349 unique values
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pixel-00017numeric7786 unique values
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pixel-00018numeric7621 unique values
0 missing
pixel-00019numeric7327 unique values
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pixel-00020numeric6803 unique values
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pixel-00022numeric5628 unique values
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pixel-00025numeric5924 unique values
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pixel-00074numeric3617 unique values
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pixel-00077numeric3153 unique values
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pixel-00094numeric3136 unique values
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pixel-00100numeric1923 unique values
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pixel-00101numeric2309 unique values
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pixel-00102numeric3099 unique values
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pixel-00104numeric2380 unique values
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pixel-00105numeric1891 unique values
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pixel-00107numeric1336 unique values
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pixel-00108numeric2308 unique values
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pixel-00109numeric2943 unique values
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pixel-00110numeric1906 unique values
0 missing
pixel-00111numeric3426 unique values
0 missing
pixel-00112numeric2817 unique values
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pixel-00113numeric4575 unique values
0 missing
pixel-00114numeric2059 unique values
0 missing
pixel-00115numeric3116 unique values
0 missing
pixel-00116numeric2407 unique values
0 missing
pixel-00117numeric3961 unique values
0 missing
pixel-00118numeric2856 unique values
0 missing
pixel-00119numeric3675 unique values
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pixel-00120numeric3545 unique values
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pixel-00121numeric3185 unique values
0 missing
pixel-00122numeric2916 unique values
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pixel-00123numeric3146 unique values
0 missing
pixel-00124numeric3122 unique values
0 missing
pixel-00125numeric2755 unique values
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pixel-00126numeric2206 unique values
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pixel-00127numeric876 unique values
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pixel-00128numeric10019 unique values
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pixel-00129numeric2809 unique values
0 missing
pixel-00130numeric3447 unique values
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pixel-00131numeric3954 unique values
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pixel-00132numeric4110 unique values
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pixel-00133numeric4200 unique values
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pixel-00134numeric4506 unique values
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pixel-00135numeric5025 unique values
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pixel-00136numeric5159 unique values
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pixel-00137numeric4720 unique values
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pixel-00138numeric5816 unique values
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pixel-00139numeric4795 unique values
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pixel-00140numeric5596 unique values
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pixel-00141numeric4825 unique values
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pixel-00142numeric6831 unique values
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pixel-00143numeric6154 unique values
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pixel-00144numeric5204 unique values
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pixel-00145numeric6957 unique values
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pixel-00146numeric6899 unique values
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pixel-00147numeric6464 unique values
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pixel-00148numeric6208 unique values
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pixel-00149numeric9009 unique values
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pixel-00150numeric7352 unique values
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pixel-00151numeric8165 unique values
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pixel-00152numeric8722 unique values
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pixel-00153numeric9540 unique values
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pixel-00154numeric9551 unique values
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pixel-00156numeric10752 unique values
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pixel-00157numeric9648 unique values
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pixel-00158numeric10053 unique values
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pixel-00159numeric10187 unique values
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pixel-00160numeric7874 unique values
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pixel-00161numeric8073 unique values
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pixel-00162numeric9236 unique values
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pixel-00163numeric8493 unique values
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pixel-00164numeric7571 unique values
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pixel-00165numeric7423 unique values
0 missing
pixel-00166numeric6919 unique values
0 missing
pixel-00167numeric5760 unique values
0 missing
pixel-00168numeric5097 unique values
0 missing
pixel-00169numeric3125 unique values
0 missing
pixel-00170numeric13628 unique values
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pixel-00171numeric2805 unique values
0 missing
pixel-00172numeric4113 unique values
0 missing
pixel-00173numeric4791 unique values
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pixel-00174numeric5350 unique values
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pixel-00175numeric5529 unique values
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pixel-00176numeric5374 unique values
0 missing
pixel-00177numeric5836 unique values
0 missing
pixel-00178numeric4154 unique values
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pixel-00179numeric6986 unique values
0 missing
pixel-00180numeric3635 unique values
0 missing
pixel-00181numeric7518 unique values
0 missing
pixel-00182numeric5169 unique values
0 missing
pixel-00183numeric4435 unique values
0 missing
pixel-00184numeric8270 unique values
0 missing
pixel-00185numeric3978 unique values
0 missing
pixel-00186numeric4492 unique values
0 missing
pixel-00187numeric5735 unique values
0 missing
pixel-00188numeric4880 unique values
0 missing
pixel-00189numeric4328 unique values
0 missing
pixel-00190numeric3562 unique values
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pixel-00191numeric9074 unique values
0 missing
pixel-00192numeric2382 unique values
0 missing
pixel-00193numeric3859 unique values
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pixel-00194numeric4835 unique values
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pixel-00195numeric3130 unique values
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pixel-00196numeric5458 unique values
0 missing
pixel-00197numeric4323 unique values
0 missing
pixel-00198numeric7319 unique values
0 missing
pixel-00199numeric2917 unique values
0 missing
pixel-00200numeric4443 unique values
0 missing
pixel-00201numeric3258 unique values
0 missing
pixel-00202numeric5756 unique values
0 missing
pixel-00203numeric3787 unique values
0 missing
pixel-00204numeric4894 unique values
0 missing
pixel-00205numeric4624 unique values
0 missing
pixel-00206numeric3956 unique values
0 missing
pixel-00207numeric3478 unique values
0 missing
pixel-00208numeric3749 unique values
0 missing
pixel-00209numeric3509 unique values
0 missing
pixel-00210numeric2702 unique values
0 missing
pixel-00211numeric1820 unique values
0 missing
pixel-00212numeric531 unique values
0 missing
pixel-00213numeric10446 unique values
0 missing
pixel-00214numeric1878 unique values
0 missing
pixel-00215numeric2857 unique values
0 missing
pixel-00216numeric3639 unique values
0 missing
pixel-00217numeric3800 unique values
0 missing
pixel-00218numeric3825 unique values
0 missing
pixel-00219numeric4229 unique values
0 missing
pixel-00220numeric4914 unique values
0 missing
pixel-00221numeric5188 unique values
0 missing
pixel-00222numeric4052 unique values
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pixel-00223numeric6047 unique values
0 missing
pixel-00224numeric3738 unique values
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pixel-00225numeric4910 unique values
0 missing
pixel-00226numeric3445 unique values
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pixel-00227numeric7152 unique values
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pixel-00228numeric5151 unique values
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pixel-00229numeric3447 unique values
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pixel-00230numeric6241 unique values
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pixel-00231numeric5583 unique values
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pixel-00232numeric4640 unique values
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pixel-00233numeric3626 unique values
0 missing
pixel-00234numeric8772 unique values
0 missing
pixel-00235numeric4759 unique values
0 missing
pixel-00236numeric5600 unique values
0 missing
pixel-00237numeric5897 unique values
0 missing
pixel-00238numeric6752 unique values
0 missing
pixel-00239numeric5803 unique values
0 missing
pixel-00240numeric5665 unique values
0 missing
pixel-00241numeric7983 unique values
0 missing
pixel-00242numeric6086 unique values
0 missing
pixel-00243numeric6971 unique values
0 missing
pixel-00244numeric7696 unique values
0 missing
pixel-00245numeric5929 unique values
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pixel-00246numeric6579 unique values
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pixel-00247numeric8106 unique values
0 missing
pixel-00248numeric7948 unique values
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pixel-00249numeric7688 unique values
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pixel-00250numeric7924 unique values
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pixel-00251numeric8164 unique values
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pixel-00252numeric7939 unique values
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pixel-00253numeric8232 unique values
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pixel-00254numeric7231 unique values
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pixel-00255numeric15760 unique values
0 missing
Show first 100 features

62 properties

NumberOfInstances
51839
Number of instances (rows) of the dataset.
NumberOfFeatures
257
Number of attributes (columns) of the dataset.
NumberOfClasses
43
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
256
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
MaxSkewnessOfNumericAtts
25.67
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
6.33
Third quartile of skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.09
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.52
Percentage of instances belonging to the least frequent class.
Quartile1KurtosisOfNumericAtts
23.52
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.01
Third quartile of standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
270
Number of instances belonging to the least frequent class.
Quartile1MeansOfNumericAtts
0
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
MeanKurtosisOfNumericAtts
97.8
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
MeanMeansOfNumericAtts
0
Mean of means among attributes of the numeric type.
Quartile1SkewnessOfNumericAtts
3.74
First quartile of skewness among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
Quartile1StdDevOfNumericAtts
0
First quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.77
Average class difference between consecutive instances.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
ClassEntropy
5.02
Entropy of the target attribute values.
MeanNominalAttDistinctValues
43
Average number of distinct values among the attributes of the nominal type.
Quartile2KurtosisOfNumericAtts
40.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
Dimensionality
0
Number of attributes divided by the number of instances.
MeanSkewnessOfNumericAtts
5.79
Mean skewness among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0
Second quartile (Median) of means among attributes of the numeric type.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
5.79
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
0.01
Mean standard deviation of attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
MajorityClassSize
3000
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2SkewnessOfNumericAtts
4.73
Second quartile (Median) of skewness among attributes of the numeric type.
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
5.75
Minimum kurtosis among attributes of the numeric type.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
MaxKurtosisOfNumericAtts
1555.3
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
MaxMeansOfNumericAtts
0.08
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
85.58
Third quartile of kurtosis among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
43
The minimal number of distinct values among attributes of the nominal type.
PercentageOfNumericFeatures
99.61
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
0.01
Third quartile of means among attributes of the numeric type.
MaxNominalAttDistinctValues
43
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
2.01
Minimum skewness among attributes of the numeric type.
PercentageOfSymbolicFeatures
0.39
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Show all 62 properties

13 tasks

Supervised Classification on GTSRB-HueHist
1 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on GTSRB-HueHist
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on GTSRB-HueHist
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on GTSRB-HueHist
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on GTSRB-HueHist
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
Clustering on GTSRB-HueHist
0 runs - estimation_procedure: 50 times Clustering
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