Data
GTSRB-HOG03

GTSRB-HOG03

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. \# HOG features The file contains three sets of differently configured HOG features (Histograms of Oriented Gradients). The sets contain feature vectors of length 1568, 1568, and 2916 respectively. The features were calculated using the source code from http://pascal.inrialpes.fr/soft/olt/. For detailed information on HOG, we refer to N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition, pages 886-893, 2005

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62 properties

51839
Number of instances (rows) of the dataset.
2917
Number of attributes (columns) of the dataset.
43
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
2916
Number of numeric attributes.
1
Number of nominal attributes.
1.44
First quartile of skewness among attributes of the numeric type.
0.09
Mean of means among attributes of the numeric type.
0.07
First quartile of standard deviation of attributes of the numeric type.
0.77
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
5.02
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4.17
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.06
Number of attributes divided by the number of instances.
43
Average number of distinct values among the attributes of the nominal type.
0.08
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
5.79
Percentage of instances belonging to the most frequent class.
0.1
Mean standard deviation of attributes of the numeric type.
1.89
Second quartile (Median) of skewness among attributes of the numeric type.
3000
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.09
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.5
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
68.15
Maximum kurtosis among attributes of the numeric type.
0.01
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
7.85
Third quartile of kurtosis among attributes of the numeric type.
0.41
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
99.97
Percentage of numeric attributes.
0.12
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
43
The minimal number of distinct values among attributes of the nominal type.
0.03
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
43
The maximum number of distinct values among attributes of the nominal type.
-0.65
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.5
Third quartile of skewness among attributes of the numeric type.
6.62
Maximum skewness among attributes of the numeric type.
0.03
Minimum standard deviation of attributes of the numeric type.
1.97
First quartile of kurtosis among attributes of the numeric type.
0.12
Third quartile of standard deviation of attributes of the numeric type.
0.25
Maximum standard deviation of attributes of the numeric type.
0.52
Percentage of instances belonging to the least frequent class.
270
Number of instances belonging to the least frequent class.
0.05
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
6.08
Mean kurtosis among attributes of the numeric type.

2 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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