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gina_agnostic

gina_agnostic

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Felicia West
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  • derived mythbusting_1 OpenML100 study_1 study_123 study_135 study_14 study_15 study_20 study_34 study_41 study_225
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Author: [Isabelle Guyon](isabelle@clopinet.com) Source: [Agnostic Learning vs. Prior Knowledge Challenge](http://www.agnostic.inf.ethz.ch) Please cite: None Dataset from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch), which consisted of 5 different datasets (SYLVA, GINA, NOVA, HIVA, ADA). The purpose of the challenge was to check if the performance of domain-specific feature engineering (prior knowledge) can be met by algorithms that were trained on data without any domain-specific knowledge (agnostic). For the latter, the data was anonymised and preprocessed in a way that makes them uninterpretable. Modified by TunedIT (converted to ARFF format) ### Topic The task of GINA is handwritten digit recognition. This is the agnostic version of a subset of the MNIST data set. We chose the problem of separating the odd numbers from even numbers. We use 2-digit numbers. Only the unit digit is informative for that task, therefore at least ½ of the features are distracters. This is a twoclass classification problem with sparse continuous input variables, in which each class is composed of several clusters. It is a problems with heterogeneous classes. ### Source The data set was constructed from the MNIST data that is made available by Yann LeCun of the NEC Research Institute at [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/). The digits have been size-normalized and centered in a fixed-size image of dimension 28x28. Examples are shown in the [documentation in chapter 3](http://clopinet.com/isabelle/Projects/agnostic/Dataset.pdf). ### Description To construct the “agnostic” dataset, we performed the following steps: - We removed the pixels that were 99% of the time white. This reduced the original feature set of 784 pixels to 485. - The original resolution (256 gray levels) was kept. - In spite of the fact that the data are rather sparse (about 30% of the values are non-zero), we saved the data as a dense matrix because we found that it can be compressed better in this way (to 19 MB.) - The feature names are the (i,j) matrix coordinates of the pixels (in a 28x28 matrix.) - We created 2 digit numbers by dividing the datasets into to parts and pairing the digits at random. - The task is to separate odd from even numbers. The digit of the tens being not informative, the features of that digit act as distracters. To construct the “prior” dataset, we went back to the original data and fetched the “informative” digit in its original representation. Therefore, this data representation consists in a vector of concatenating the lines of a 28x28 pixel map. Data type: non-sparse Number of features: 970 Number of examples and check-sums: Pos_ex Neg_ex Tot_ex Check_sum Train 1550 1603 3153 164947945.00 Valid 155 160 315 16688946.00 This dataset contains samples from both training and validation datasets.

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0 missing
Show first 100 features

107 properties

NumberOfInstances
3468
Number of instances (rows) of the dataset.
NumberOfFeatures
971
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
970
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.5
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.86
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
0.15
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
0.7
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
0.86
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
0.15
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
0.7
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
0.86
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
0.15
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
0.7
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
1
Entropy of the target attribute values.
DecisionStumpAUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.28
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.
J48.00001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
50.84
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1763
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
283.86
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
140.4
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
16.12
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
114.03
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
18.65
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
53.99
Mean of means among attributes of the numeric type.
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
2.93
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
77.25
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.83
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.93
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
-0.24
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
12.22
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
49.16
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1705
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
0.23
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
0.1
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.9
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.1
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.53
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
11.61
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.52
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
46.44
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
46.79
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
1.59
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
88.14
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
17.19
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
93.5
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
4.25
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
108.61
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.51
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

27 tasks

Supervised Classification on gina_agnostic
66404 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: label
Supervised Classification on gina_agnostic
124 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
Supervised Classification on gina_agnostic
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: label
Supervised Classification on gina_agnostic
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: label
Supervised Classification on gina_agnostic
0 runs - estimation_procedure: 33% Holdout set - target_feature: label
Learning Curve on gina_agnostic
81 runs - estimation_procedure: 10-fold Learning Curve - target_feature: label
Supervised Data Stream Classification on gina_agnostic
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: label
Clustering on gina_agnostic
0 runs - target_feature: label
Clustering on gina_agnostic
0 runs
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Clustering on gina_agnostic
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on gina_agnostic
1314 runs - target_feature: label
Subgroup Discovery on gina_agnostic
1311 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
Subgroup Discovery on gina_agnostic
0 runs - target_feature: label
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