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scene

scene

active ARFF Publicly available Visibility: public Uploaded 25-08-2014 by Tobias Kuehn
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Author: Source: Unknown - Please cite: Scene recognition dataset Source: Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004. 1: Description. Scenes data set contains characteristics about images and thier classes. One image can belong to one or more classes. 2: Type: Multi label 3: Origin: Real world 4: Instances: 2407 5: Features: 294 6: Labels: 6 7: Missing values: No

300 features

Urban (target)nominal2 unique values
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attr1numeric2349 unique values
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attr283numeric2358 unique values
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attr285numeric2370 unique values
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attr286numeric2366 unique values
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attr287numeric2357 unique values
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attr288numeric2347 unique values
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attr289numeric2354 unique values
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attr290numeric2357 unique values
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attr291numeric2346 unique values
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attr292numeric2368 unique values
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attr293numeric2359 unique values
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attr294numeric2354 unique values
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Beachnominal2 unique values
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Sunsetnominal2 unique values
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FallFoliagenominal2 unique values
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Fieldnominal2 unique values
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Mountainnominal2 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
2407
Number of instances (rows) of the dataset.
NumberOfFeatures
300
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
294
Number of numeric attributes.
NumberOfSymbolicFeatures
6
Number of nominal attributes.
AutoCorrelation
0.98
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.87
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
CfsSubsetEval_DecisionStumpErrRate
0.08
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
CfsSubsetEval_DecisionStumpKappa
0.74
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
CfsSubsetEval_NaiveBayesAUC
0.87
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
CfsSubsetEval_NaiveBayesErrRate
0.08
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
CfsSubsetEval_NaiveBayesKappa
0.74
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
CfsSubsetEval_kNN1NAUC
0.87
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
CfsSubsetEval_kNN1NErrRate
0.08
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
CfsSubsetEval_kNN1NKappa
0.74
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
ClassEntropy
0.68
Entropy of the target attribute values.
DecisionStumpAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.12
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
14.43
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
82.09
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1976
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
0.76
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
76.68
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.72
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.07
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
7.31
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.26
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
0.68
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
8.19
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.31
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.05
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
13.37
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
1.69
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.18
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.61
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.75
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.03
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.02
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
-0.85
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.07
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
17.91
Percentage of instances belonging to the least frequent class.
MinorityClassSize
431
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
6
Number of binary attributes.
PercentageOfBinaryFeatures
2
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
98
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.63
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.26
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.13
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.05
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.16
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
0.67
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
2.39
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.27
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.05
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.34
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.19
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
0.72
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
6.96
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.51
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
2.45
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.21
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Supervised Classification on scene
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: Leave one out - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: 33% Holdout set - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: Test on Training Data - target_feature: Urban
Supervised Classification on scene
0 runs - estimation_procedure: 10% Holdout set - target_feature: Urban
Learning Curve on scene
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Urban
Learning Curve on scene
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Urban
Supervised Data Stream Classification on scene
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Urban
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