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banknote-authentication

banknote-authentication

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_135 study_14 study_34 study_50 study_52 study_7 study_98 study_99 uci
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Author: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/banknote+authentication) - 2012 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Dataset about distinguishing genuine and forged banknotes. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. A Wavelet Transform tool was used to extract features from these images. ### Attribute Information V1. variance of Wavelet Transformed image (continuous) V2. skewness of Wavelet Transformed image (continuous) V3. curtosis of Wavelet Transformed image (continuous) V4. entropy of image (continuous) Class (target). Presumably 1 for genuine and 2 for forged

5 features

Class (target)nominal2 unique values
0 missing
V1numeric1338 unique values
0 missing
V2numeric1256 unique values
0 missing
V3numeric1270 unique values
0 missing
V4numeric1156 unique values
0 missing

62 properties

1372
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
2
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.
4
Number of numeric attributes.
1
Number of nominal attributes.
Maximum entropy among attributes.
-0.75
Minimum kurtosis among attributes of the numeric type.
20
Percentage of binary attributes.
3.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.27
Maximum kurtosis among attributes of the numeric type.
-1.19
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
1.08
Third quartile of kurtosis among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
80
Percentage of numeric attributes.
1.79
Third quartile of means among attributes of the numeric type.
2
The maximum number of distinct values among attributes of the nominal type.
-1.02
Minimum skewness among attributes of the numeric type.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
1.09
Maximum skewness among attributes of the numeric type.
2.1
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.78
Third quartile of skewness among attributes of the numeric type.
5.87
Maximum standard deviation of attributes of the numeric type.
44.46
Percentage of instances belonging to the least frequent class.
-0.67
First quartile of kurtosis among attributes of the numeric type.
5.48
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
610
Number of instances belonging to the least frequent class.
-0.79
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.
0.14
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.64
Mean of means among attributes of the numeric type.
-0.87
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
2.29
First quartile of standard deviation of attributes of the numeric type.
0.99
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.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.03
Second quartile (Median) of kurtosis 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.
-0.12
Mean skewness among attributes of the numeric type.
0.92
Second quartile (Median) of means among attributes of the numeric type.
55.54
Percentage of instances belonging to the most frequent class.
3.78
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
762
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.27
Second quartile (Median) of skewness among attributes of the numeric type.

21 tasks

84654 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
43620 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - evaluation_measure: area_under_roc_curve - target_feature: Class
0 runs - estimation_procedure: Custom Holdout - evaluation_measure: area_under_roc_curve - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: area_under_roc_curve - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
1297 runs - target_feature: Class
1297 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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