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Satellite

Satellite

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Author: Markus Goldstein Source: [Dataverse](http://www.madm.eu/downloads https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF) Please cite: The satellite dataset comprises of features extracted from satellite observations. In particular, each image was taken under four different light wavelength, two in visible light (green and red) and two infrared images. The task of the original dataset is to classify the image into the soil category of the observed region. ### Classes We defined the soil classes “red soil”, “gray soil”, “damp gray soil” and “very damp gray soil” as the normal class. From the semantically different classes “cotton crop” and “soil with vegetation stubble” anomalies are sampled. After merging the original training and test set into a single dataset, the resulting dataset contains 5,025 normal instances as well as 75 randomly sampled anomalies (1.49%) with 36 dimensions ### Relevant Papers Goldstein, Markus, and Seiichi Uchida. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data." PloS one 11.4 (2016): e0152173 This dataset is not the original dataset. The target variable 'Target' is relabeled into 'Normal' and 'Anomaly'

37 features

Target (target)nominal2 unique values
0 missing
V20numeric75 unique values
0 missing
V19numeric67 unique values
0 missing
V21numeric47 unique values
0 missing
V22numeric73 unique values
0 missing
V23numeric69 unique values
0 missing
V24numeric84 unique values
0 missing
V25numeric49 unique values
0 missing
V26numeric78 unique values
0 missing
V27numeric69 unique values
0 missing
V28numeric78 unique values
0 missing
V29numeric48 unique values
0 missing
V30numeric78 unique values
0 missing
V31numeric68 unique values
0 missing
V32numeric78 unique values
0 missing
V33numeric49 unique values
0 missing
V34numeric76 unique values
0 missing
V35numeric70 unique values
0 missing
V36numeric80 unique values
0 missing
V10numeric77 unique values
0 missing
V2numeric81 unique values
0 missing
V3numeric69 unique values
0 missing
V4numeric78 unique values
0 missing
V5numeric48 unique values
0 missing
V6numeric76 unique values
0 missing
V7numeric68 unique values
0 missing
V8numeric76 unique values
0 missing
V9numeric47 unique values
0 missing
V1numeric49 unique values
0 missing
V11numeric69 unique values
0 missing
V12numeric81 unique values
0 missing
V13numeric49 unique values
0 missing
V14numeric77 unique values
0 missing
V15numeric67 unique values
0 missing
V16numeric73 unique values
0 missing
V17numeric49 unique values
0 missing
V18numeric72 unique values
0 missing
Show all 37 features

62 properties

NumberOfInstances
5100
Number of instances (rows) of the dataset.
NumberOfFeatures
37
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
36
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
2.7
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.3
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.7
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.88
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
74.66
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-0.3
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
12.35
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
85.34
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.09
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
14.66
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.09
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
97.14
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.07
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
15.73
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
85.71
Mean of means among attributes of the numeric type.
ClassEntropy
0.11
Entropy of the target attribute values.
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
98.53
Percentage of instances belonging to the most frequent class.
MajorityClassSize
5025
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.08
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
99.57
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.12
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
16.22
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.46
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.11
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
14.24
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
72.36
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.41
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
11.66
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
1.47
Percentage of instances belonging to the least frequent class.
MinorityClassSize
75
Number of instances belonging to the least frequent class.
Show all 62 properties

16 tasks

Supervised Classification on Satellite
2078 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Target
Supervised Classification on Satellite
0 runs - estimation_procedure: 33% Holdout set - target_feature: Target
Supervised Classification on Satellite
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Target
Supervised Classification on Satellite
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Target
Supervised Classification on Satellite
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: f_measure - target_feature: Target
Supervised Classification on Satellite
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Target
Clustering on Satellite
0 runs
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
Clustering on Satellite
0 runs - estimation_procedure: 50 times Clustering
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