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gina_agnostic

gina_agnostic

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • derived mythbusting_1 OpenML100 study_1 study_123 study_135 study_14 study_15 study_20 study_34 study_41
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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
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
attr508numeric93 unique values
0 missing
attr764numeric254 unique values
0 missing
attr253numeric34 unique values
0 missing
attr509numeric201 unique values
0 missing
attr765numeric248 unique values
0 missing
attr254numeric223 unique values
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0 missing

107 properties

3468
Number of instances (rows) of the dataset.
971
Number of attributes (columns) of the dataset.
2
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.
970
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.52
First quartile of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.93
Mean skewness among attributes of the numeric type.
46.44
First quartile of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
77.25
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50.84
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1
Entropy of the target attribute values.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1763
Number of instances belonging to the most frequent class.
-1.83
Minimum kurtosis among attributes of the numeric type.
46.79
Second quartile (Median) of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.93
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
283.86
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.59
Second quartile (Median) of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
140.4
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0.1
Percentage of binary attributes.
88.14
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.28
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.24
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
12.22
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
17.19
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
16.12
Maximum skewness among attributes of the numeric type.
49.16
Percentage of instances belonging to the least frequent class.
99.9
Percentage of numeric attributes.
93.5
Third quartile of means among attributes of the numeric type.
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
114.03
Maximum standard deviation of attributes of the numeric type.
1705
Number of instances belonging to the least frequent class.
0.1
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
4.25
Third quartile of skewness among attributes of the numeric type.
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
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
18.65
Mean kurtosis among attributes of the numeric type.
0.23
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.53
First quartile of kurtosis among attributes of the numeric type.
108.61
Third quartile of standard deviation of attributes of the numeric type.
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
53.99
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
11.61
First quartile of means among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

17 tasks

62696 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: label
124 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: label
0 runs - estimation_procedure: 33% Holdout set - target_feature: label
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: label
81 runs - estimation_procedure: 10-fold Learning Curve - target_feature: label
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: label
0 runs - target_feature: label
1314 runs - target_feature: label
1311 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
0 runs - target_feature: label
Define a new task