Data
seismic-bumps

seismic-bumps

in_preparation ARFF Publicly available Visibility: public Uploaded 23-08-2017 by Rafael G. Mantovani
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The data describe the problem of high energy (higher than 10^4 J) seismic bumps forecasting in a coal mine. Data come from two of longwalls located in a Polish coal mine.

19 features

class (target)nominal2 unique values
0 missing
seismicnominal2 unique values
0 missing
seismoacousticnominal3 unique values
0 missing
shiftnominal2 unique values
0 missing
genergynumeric2212 unique values
0 missing
gpulsnumeric1128 unique values
0 missing
gdenergynumeric334 unique values
0 missing
gdpulsnumeric292 unique values
0 missing
ghazardnominal3 unique values
0 missing
nbumpsnumeric10 unique values
0 missing
nbumps2numeric7 unique values
0 missing
nbumps3numeric7 unique values
0 missing
nbumps4numeric4 unique values
0 missing
nbumps5numeric2 unique values
0 missing
nbumps6numeric1 unique values
0 missing
nbumps7numeric1 unique values
0 missing
nbumps89numeric1 unique values
0 missing
energynumeric242 unique values
0 missing
maxenergynumeric33 unique values
0 missing

62 properties

2584
Number of instances (rows) of the dataset.
19
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.
14
Number of numeric attributes.
5
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
2.4
Average number of distinct values among the attributes of the nominal type.
24.97
Second quartile (Median) of kurtosis among attributes of the numeric type.
57.37
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5.56
Mean skewness among attributes of the numeric type.
0.63
Second quartile (Median) of means among attributes of the numeric type.
93.42
Percentage of instances belonging to the most frequent class.
19265.59
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
2414
Number of instances belonging to the most frequent class.
0.5
Minimal entropy among attributes.
3.41
Second quartile (Median) of skewness among attributes of the numeric type.
1.07
Maximum entropy among attributes.
5.93
Minimum kurtosis among attributes of the numeric type.
10.53
Percentage of binary attributes.
1.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
210.75
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.04
Third quartile of entropy among attributes.
90242.52
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
132.34
Third quartile of kurtosis among attributes of the numeric type.
0.02
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
73.68
Percentage of numeric attributes.
1473.65
Third quartile of means among attributes of the numeric type.
3
The maximum number of distinct values among attributes of the nominal type.
2.23
Minimum skewness among attributes of the numeric type.
26.32
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
14.58
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.61
First quartile of entropy among attributes.
9.94
Third quartile of skewness among attributes of the numeric type.
229200.51
Maximum standard deviation of attributes of the numeric type.
6.58
Percentage of instances belonging to the least frequent class.
8.64
First quartile of kurtosis among attributes of the numeric type.
5261.35
Third quartile of standard deviation of attributes of the numeric type.
0.86
Average entropy of the attributes.
170
Number of instances belonging to the least frequent class.
0
First quartile of means among attributes of the numeric type.
0.55
Standard deviation of the number of distinct values among attributes of the nominal type.
58.07
Mean kurtosis among attributes of the numeric type.
2
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
7146.7
Mean of means among attributes of the numeric type.
2.56
First quartile of skewness among attributes of the numeric type.
0.89
Average class difference between consecutive instances.
0.01
Average mutual information between the nominal attributes and the target attribute.
0.05
First quartile of standard deviation of attributes of the numeric type.
0.35
Entropy of the target attribute values.
140.12
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.94
Second quartile (Median) of entropy among attributes.

15 tasks

0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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