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steel-plates-fault

steel-plates-fault

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • OpenML100 study_123 study_14 study_34 study_50 study_52 study_7
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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. 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 7 binary features indicating the type of fault (on of 7: Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults). The latter is commonly used as a binary classification target ('common' or 'other' 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 * V28: Pastry * V29: Z_Scratch * V30: K_Scatch * V31: Stains * V32: Dirtiness * V33: Bumps * Class: Other_Faults ### 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

34 features

Class (target)nominal2 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
V28numeric2 unique values
0 missing
V29numeric2 unique values
0 missing
V30numeric2 unique values
0 missing
V31numeric2 unique values
0 missing
V32numeric2 unique values
0 missing
V33numeric2 unique values
0 missing

107 properties

1941
Number of instances (rows) of the dataset.
34
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.
33
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.21
First quartile of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.65
Mean skewness among attributes of the numeric type.
0.27
First quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
123291.95
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
65.33
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.93
Entropy of the target attribute values.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1268
Number of instances belonging to the most frequent class.
-1.86
Minimum kurtosis among attributes of the numeric type.
0.61
Second quartile (Median) of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.13
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.35
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1663.05
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.85
Second quartile (Median) of skewness among attributes of the numeric type.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1650738.71
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
2.94
Percentage of binary attributes.
0.48
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.93
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
0.06
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.36
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
39.29
Maximum skewness among attributes of the numeric type.
34.67
Percentage of instances belonging to the least frequent class.
97.06
Percentage of numeric attributes.
121.02
Third quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1774590.09
Maximum standard deviation of attributes of the numeric type.
673
Number of instances belonging to the least frequent class.
2.94
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.94
Third quartile of skewness among attributes of the numeric type.
0.97
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
91.65
Mean kurtosis among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.06
First quartile of kurtosis among attributes of the numeric type.
222.89
Third quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
106447.79
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.2
First quartile of means among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

15 tasks

139040 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
132227 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
46 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1307 runs - target_feature: Class
1305 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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