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steel-plates-fault

steel-plates-fault

active ARFF Publicly available Visibility: public Uploaded 04-12-2017 by Jann Goschenhofer
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  • OpenML-CC18 study_135 study_98 study_99
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Author: Semeion, Research Center of Sciences of Communication, Rome, Italy. Source: [UCI](http://archive.ics.uci.edu/ml/datasets/steel+plates+faults) Please cite: Dataset provided by Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy. __Changes w.r.t. version 1: included one target factor with 7 levels as target variable for the classification. Also deleted the previous 7 binary target variables.__ Steel Plates Faults Data Set A dataset of steel plates' faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition. The dataset consists of 27 features describing each fault (location, size, ...) and 1 feature indicating the type of fault (on of 7: Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults). The target is the type of fault. ### Attribute Information * V1: X_Minimum * V2: X_Maximum * V3: Y_Minimum * V4: Y_Maximum * V5: Pixels_Areas * V6: X_Perimeter * V7: Y_Perimeter * V8: Sum_of_Luminosity * V9: Minimum_of_Luminosity * V10: Maximum_of_Luminosity * V11: Length_of_Conveyer * V12: TypeOfSteel_A300 * V13: TypeOfSteel_A400 * V14: Steel_Plate_Thickness * V15: Edges_Index * V16: Empty_Index * V17: Square_Index * V18: Outside_X_Index * V19: Edges_X_Index * V20: Edges_Y_Index * V21: Outside_Global_Index * V22: LogOfAreas * V23: Log_X_Index * V24: Log_Y_Index * V25: Orientation_Index * V26: Luminosity_Index * V27: SigmoidOfAreas * target: 7 types of fault as classification target ### Relevant Papers 1.M Buscema, S Terzi, W Tastle, A New Meta-Classifier,in NAFIPS 2010, Toronto (CANADA),26-28 July 2010, 978-1-4244-7858-6/10 ©2010 IEEE 2.M Buscema, MetaNet: The Theory of Independent Judges, in Substance Use & Misuse, 33(2), 439-461,1998

28 features

target (target)nominal7 unique values
0 missing
V1numeric962 unique values
0 missing
V2numeric994 unique values
0 missing
V3numeric1939 unique values
0 missing
V4numeric1940 unique values
0 missing
V5numeric920 unique values
0 missing
V6numeric399 unique values
0 missing
V7numeric317 unique values
0 missing
V8numeric1909 unique values
0 missing
V9numeric161 unique values
0 missing
V10numeric100 unique values
0 missing
V11numeric84 unique values
0 missing
V12numeric2 unique values
0 missing
V13numeric2 unique values
0 missing
V14numeric24 unique values
0 missing
V15numeric1387 unique values
0 missing
V16numeric1338 unique values
0 missing
V17numeric770 unique values
0 missing
V18numeric454 unique values
0 missing
V19numeric818 unique values
0 missing
V20numeric648 unique values
0 missing
V21numeric3 unique values
0 missing
V22numeric914 unique values
0 missing
V23numeric183 unique values
0 missing
V24numeric217 unique values
0 missing
V25numeric918 unique values
0 missing
V26numeric1522 unique values
0 missing
V27numeric388 unique values
0 missing

62 properties

1941
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
7
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.
27
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.5
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.86
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1663.05
Maximum kurtosis among attributes of the numeric type.
-0.13
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
11.36
Third quartile of kurtosis among attributes of the numeric type.
1650738.71
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
96.43
Percentage of numeric attributes.
571.14
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
3.57
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
-0.93
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.81
Third quartile of skewness among attributes of the numeric type.
39.29
Maximum skewness among attributes of the numeric type.
0.06
Minimum standard deviation of attributes of the numeric type.
-1.15
First quartile of kurtosis among attributes of the numeric type.
426.48
Third quartile of standard deviation of attributes of the numeric type.
1774590.09
Maximum standard deviation of attributes of the numeric type.
2.83
Percentage of instances belonging to the least frequent class.
0.57
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.
55
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
109.59
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.06
First quartile of skewness among attributes of the numeric type.
130102.83
Mean of means among attributes of the numeric type.
0.3
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.41
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.
-0.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
7
Average number of distinct values among the attributes of the nominal type.
1.4
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.
3.74
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
34.67
Percentage of instances belonging to the most frequent class.
150690.09
Mean standard deviation of attributes of the numeric type.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
673
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

7 tasks

4217 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: target
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: target
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: 33% Holdout set - target_feature: target
0 runs - estimation_procedure: 50 times Clustering
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