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Multi-label dataset. The image benchmark dataset consists of 2000 natural scene images. Zhou and Zhang (2007) extracted 135 features for each image and made it publicly available as processed image dataset. Each observation can be associated with different label sets, where all possible labels are {desert, mountains, sea, sunset, trees}. About 22% of the images belong to more than one class. However, images belonging to three classes or more are very rare.

140 features

desert (target)nominal2 unique values
0 missing
mountains (target)nominal2 unique values
0 missing
sea (target)nominal2 unique values
0 missing
sunset (target)nominal2 unique values
0 missing
trees (target)nominal2 unique values
0 missing
Feature1numeric808 unique values
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Feature2numeric821 unique values
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Feature3numeric809 unique values
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Feature4numeric780 unique values
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Feature5numeric792 unique values
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Feature25numeric705 unique values
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Feature26numeric731 unique values
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Feature27numeric722 unique values
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Feature30numeric615 unique values
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Feature39numeric574 unique values
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Feature45numeric550 unique values
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Feature81numeric745 unique values
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Feature82numeric615 unique values
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Feature94numeric557 unique values
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Feature95numeric600 unique values
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Feature96numeric598 unique values
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Feature97numeric550 unique values
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Feature98numeric554 unique values
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Feature100numeric545 unique values
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Feature101numeric564 unique values
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Feature102numeric586 unique values
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Feature103numeric515 unique values
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Feature104numeric545 unique values
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Feature105numeric565 unique values
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Feature106numeric493 unique values
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Feature107numeric511 unique values
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Feature111numeric860 unique values
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Feature112numeric814 unique values
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Feature113numeric823 unique values
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Feature114numeric825 unique values
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Feature115numeric789 unique values
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Feature116numeric791 unique values
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Feature117numeric803 unique values
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Feature118numeric754 unique values
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Feature119numeric775 unique values
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Feature120numeric767 unique values
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Feature121numeric719 unique values
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Feature122numeric722 unique values
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Feature123numeric710 unique values
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Feature124numeric699 unique values
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Feature125numeric722 unique values
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Feature126numeric712 unique values
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Feature127numeric735 unique values
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Feature128numeric736 unique values
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Feature129numeric745 unique values
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Feature130numeric691 unique values
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Feature131numeric691 unique values
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Feature132numeric696 unique values
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Feature133numeric674 unique values
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Feature134numeric695 unique values
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Feature135numeric683 unique values
0 missing
Show first 100 features

62 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
140
Number of attributes (columns) of the dataset.
NumberOfClasses
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
135
Number of numeric attributes.
NumberOfSymbolicFeatures
5
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
3.57
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
96.43
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
3.57
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.08
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-14.71
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
-0.09
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
36.88
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-1.4
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
0.03
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
46.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
2.02
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
24.74
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
0.17
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
54.29
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
19.08
Mean of means among attributes of the numeric type.
ClassEntropy
Entropy of the target attribute values.
Dimensionality
0.07
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
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
5.32
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
130.2
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
0.64
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
64.24
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.11
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
5
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
0.05
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
46.09
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.13
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-30.86
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
-0.54
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
28.59
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
Show all 62 properties

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Clustering on image
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Clustering on image
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Clustering on image
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Clustering on image
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Clustering on image
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Clustering on image
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