Data
Bank-Note-Authentication-UCI

Bank-Note-Authentication-UCI

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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  • Computer Systems Machine Learning
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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. Wavelet Transform tool were used to extract features from images.

5 features

class (target)numeric2 unique values
0 missing
variancenumeric1338 unique values
0 missing
skewnessnumeric1256 unique values
0 missing
curtosisnumeric1270 unique values
0 missing
entropynumeric1156 unique values
0 missing

19 properties

1372
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
0
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.
5
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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