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active ARFF Publicly available Visibility: public Uploaded 03-04-2019 by Quay Au
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  • 2019_multioutput_paper_benchmark_data
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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
0 missing
Feature2numeric821 unique values
0 missing
Feature3numeric809 unique values
0 missing
Feature4numeric780 unique values
0 missing
Feature5numeric792 unique values
0 missing
Feature6numeric790 unique values
0 missing
Feature7numeric788 unique values
0 missing
Feature8numeric788 unique values
0 missing
Feature9numeric782 unique values
0 missing
Feature10numeric704 unique values
0 missing
Feature11numeric706 unique values
0 missing
Feature12numeric717 unique values
0 missing
Feature13numeric684 unique values
0 missing
Feature14numeric681 unique values
0 missing
Feature15numeric678 unique values
0 missing
Feature16numeric677 unique values
0 missing
Feature17numeric701 unique values
0 missing
Feature18numeric686 unique values
0 missing
Feature19numeric804 unique values
0 missing
Feature20numeric811 unique values
0 missing
Feature21numeric793 unique values
0 missing
Feature22numeric765 unique values
0 missing
Feature23numeric764 unique values
0 missing
Feature24numeric763 unique values
0 missing
Feature25numeric705 unique values
0 missing
Feature26numeric731 unique values
0 missing
Feature27numeric722 unique values
0 missing
Feature28numeric632 unique values
0 missing
Feature29numeric633 unique values
0 missing
Feature30numeric615 unique values
0 missing
Feature31numeric626 unique values
0 missing
Feature32numeric640 unique values
0 missing
Feature33numeric631 unique values
0 missing
Feature34numeric610 unique values
0 missing
Feature35numeric629 unique values
0 missing
Feature36numeric600 unique values
0 missing
Feature37numeric581 unique values
0 missing
Feature38numeric587 unique values
0 missing
Feature39numeric574 unique values
0 missing
Feature40numeric593 unique values
0 missing
Feature41numeric603 unique values
0 missing
Feature42numeric557 unique values
0 missing
Feature43numeric554 unique values
0 missing
Feature44numeric573 unique values
0 missing
Feature45numeric550 unique values
0 missing
Feature46numeric570 unique values
0 missing
Feature47numeric558 unique values
0 missing
Feature48numeric545 unique values
0 missing
Feature49numeric565 unique values
0 missing
Feature50numeric566 unique values
0 missing
Feature51numeric515 unique values
0 missing
Feature52numeric533 unique values
0 missing
Feature53numeric533 unique values
0 missing
Feature54numeric493 unique values
0 missing
Feature55numeric846 unique values
0 missing
Feature56numeric852 unique values
0 missing
Feature57numeric861 unique values
0 missing
Feature58numeric865 unique values
0 missing
Feature59numeric879 unique values
0 missing
Feature60numeric887 unique values
0 missing
Feature61numeric852 unique values
0 missing
Feature62numeric853 unique values
0 missing
Feature63numeric860 unique values
0 missing
Feature64numeric784 unique values
0 missing
Feature65numeric777 unique values
0 missing
Feature66numeric776 unique values
0 missing
Feature67numeric773 unique values
0 missing
Feature68numeric776 unique values
0 missing
Feature69numeric795 unique values
0 missing
Feature70numeric754 unique values
0 missing
Feature71numeric775 unique values
0 missing
Feature72numeric767 unique values
0 missing
Feature73numeric722 unique values
0 missing
Feature74numeric720 unique values
0 missing
Feature75numeric722 unique values
0 missing
Feature76numeric735 unique values
0 missing
Feature77numeric736 unique values
0 missing
Feature78numeric725 unique values
0 missing
Feature79numeric735 unique values
0 missing
Feature80numeric736 unique values
0 missing
Feature81numeric745 unique values
0 missing
Feature82numeric615 unique values
0 missing
Feature83numeric642 unique values
0 missing
Feature84numeric633 unique values
0 missing
Feature85numeric631 unique values
0 missing
Feature86numeric624 unique values
0 missing
Feature87numeric637 unique values
0 missing
Feature88numeric600 unique values
0 missing
Feature89numeric608 unique values
0 missing
Feature90numeric596 unique values
0 missing
Feature91numeric574 unique values
0 missing
Feature92numeric593 unique values
0 missing
Feature93numeric618 unique values
0 missing
Feature94numeric557 unique values
0 missing
Feature95numeric600 unique values
0 missing
Feature96numeric598 unique values
0 missing
Feature97numeric550 unique values
0 missing
Feature98numeric554 unique values
0 missing
Feature99numeric538 unique values
0 missing
Feature100numeric545 unique values
0 missing
Feature101numeric564 unique values
0 missing
Feature102numeric586 unique values
0 missing
Feature103numeric515 unique values
0 missing
Feature104numeric545 unique values
0 missing
Feature105numeric565 unique values
0 missing
Feature106numeric493 unique values
0 missing
Feature107numeric511 unique values
0 missing
Feature108numeric513 unique values
0 missing
Feature109numeric852 unique values
0 missing
Feature110numeric853 unique values
0 missing
Feature111numeric860 unique values
0 missing
Feature112numeric814 unique values
0 missing
Feature113numeric823 unique values
0 missing
Feature114numeric825 unique values
0 missing
Feature115numeric789 unique values
0 missing
Feature116numeric791 unique values
0 missing
Feature117numeric803 unique values
0 missing
Feature118numeric754 unique values
0 missing
Feature119numeric775 unique values
0 missing
Feature120numeric767 unique values
0 missing
Feature121numeric719 unique values
0 missing
Feature122numeric722 unique values
0 missing
Feature123numeric710 unique values
0 missing
Feature124numeric699 unique values
0 missing
Feature125numeric722 unique values
0 missing
Feature126numeric712 unique values
0 missing
Feature127numeric735 unique values
0 missing
Feature128numeric736 unique values
0 missing
Feature129numeric745 unique values
0 missing
Feature130numeric691 unique values
0 missing
Feature131numeric691 unique values
0 missing
Feature132numeric696 unique values
0 missing
Feature133numeric674 unique values
0 missing
Feature134numeric695 unique values
0 missing
Feature135numeric683 unique values
0 missing

62 properties

2000
Number of instances (rows) of the dataset.
140
Number of attributes (columns) of the dataset.
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.
135
Number of numeric attributes.
5
Number of nominal attributes.
-0.09
First quartile of skewness among attributes of the numeric type.
19.08
Mean of means among attributes of the numeric type.
36.88
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
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.
0.91
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.07
Number of attributes divided by the number of instances.
2
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.
0.05
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
46.09
Mean standard deviation of attributes of the numeric type.
0.03
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.57
Percentage of binary attributes.
46.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.13
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
5.32
Maximum kurtosis among attributes of the numeric type.
-30.86
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
2.02
Third quartile of kurtosis among attributes of the numeric type.
130.2
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.
24.74
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
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.
2
The maximum number of distinct values among attributes of the nominal type.
-0.54
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.17
Third quartile of skewness among attributes of the numeric type.
0.64
Maximum skewness among attributes of the numeric type.
28.59
Minimum standard deviation of attributes of the numeric type.
0.08
First quartile of kurtosis among attributes of the numeric type.
54.29
Third quartile of standard deviation of attributes of the numeric type.
64.24
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
-14.71
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.
5
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
1.11
Mean kurtosis among attributes of the numeric type.

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