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mfeat-pixel

mfeat-pixel

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Author: Robert P.W. Duin, Department of Applied Physics, Delft University of Technology Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Multiple+Features) - 1998 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Multiple Features Dataset: Pixel One of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. The maps were scanned in 8 bit grey value at density of 400dpi, scanned, sharpened, and thresholded. Corresponding patterns in different datasets correspond to the same original character. 200 instances per class (for a total of 2,000 instances) have been digitized in binary images. Using this dataset, sampled versions of the original images may be obtained (15 x 16 pixels). ### Attribute Information The mfeatures represent 240 (15 x 16) pixel averages in 2 x 3 windows. ### Relevant Papers A slightly different version of the database is used in M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386. The database as is is used in: A.K. Jain, R.P.W. Duin, J. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence archive, Volume 22 Issue 1, January 2000

241 features

class (target)nominal10 unique values
0 missing
att1nominal6 unique values
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att2nominal7 unique values
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att3nominal7 unique values
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att4nominal7 unique values
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att14nominal7 unique values
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att15nominal6 unique values
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att19nominal7 unique values
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att67nominal7 unique values
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att68nominal7 unique values
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att70nominal7 unique values
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att71nominal7 unique values
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att72nominal7 unique values
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att73nominal7 unique values
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att74nominal7 unique values
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att75nominal7 unique values
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att76nominal7 unique values
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att77nominal7 unique values
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att82nominal7 unique values
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att92nominal7 unique values
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att103nominal7 unique values
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att196nominal7 unique values
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att198nominal7 unique values
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att200nominal7 unique values
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att203nominal7 unique values
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att205nominal7 unique values
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att215nominal7 unique values
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att216nominal7 unique values
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att217nominal7 unique values
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att218nominal7 unique values
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att219nominal7 unique values
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att220nominal7 unique values
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att221nominal7 unique values
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att222nominal7 unique values
0 missing
att223nominal7 unique values
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att224nominal7 unique values
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att225nominal7 unique values
0 missing
att226nominal5 unique values
0 missing
att227nominal5 unique values
0 missing
att228nominal5 unique values
0 missing
att229nominal5 unique values
0 missing
att230nominal5 unique values
0 missing
att231nominal5 unique values
0 missing
att232nominal5 unique values
0 missing
att233nominal5 unique values
0 missing
att234nominal5 unique values
0 missing
att235nominal5 unique values
0 missing
att236nominal5 unique values
0 missing
att237nominal5 unique values
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att238nominal5 unique values
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att239nominal5 unique values
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att240nominal5 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
2000
Number of instances (rows) of the dataset.
NumberOfFeatures
241
Number of attributes (columns) of the dataset.
NumberOfClasses
10
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
0
Number of numeric attributes.
NumberOfSymbolicFeatures
241
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.89
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
0.24
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
0.89
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
0.24
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
0.89
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
0.24
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
3.32
Entropy of the target attribute values.
DecisionStumpAUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.81
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.12
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
9.55
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.73
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
10
Percentage of instances belonging to the most frequent class.
MajorityClassSize
200
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
2.49
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.7
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
10
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.78
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.35
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
4.13
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
6.88
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.53
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.04
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
5
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
10
Percentage of instances belonging to the least frequent class.
MinorityClassSize
200
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.63
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.27
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.79
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.35
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.96
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.42
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.41
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.53
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

71 tasks

Supervised Classification on mfeat-pixel
12158 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on mfeat-pixel
305 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mfeat-pixel
282 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mfeat-pixel
181 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mfeat-pixel
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on mfeat-pixel
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on mfeat-pixel
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on mfeat-pixel
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Learning Curve on mfeat-pixel
323 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on mfeat-pixel
191 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on mfeat-pixel
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on mfeat-pixel
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on mfeat-pixel
0 runs - target_feature: class
Clustering on mfeat-pixel
0 runs
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Clustering on mfeat-pixel
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on mfeat-pixel
1308 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1308 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1305 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1304 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1303 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1303 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1303 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1303 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1303 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
1301 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
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Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
Subgroup Discovery on mfeat-pixel
0 runs - target_feature: class
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