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mfeat-zernike

mfeat-zernike

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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  • OpenML-CC18 OpenML100 study_1 study_123 study_14 study_34 study_37 study_41 study_52 study_7 study_70 study_76 study_98 study_99 uci
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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: Zernike One of a set of 6 datasets describing features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. 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. In this dataset, these digits are represented in terms of 47 Zernike moments. ### Attribute Information The attributes represent 47 rotation invariant Zernike moments. They can't distinguish samples of class '6' from those of class '9'. More information on Zernike moments can be found in: A. Khotanzad and Y.H. Hong: Rotation invariant pattern recognition using Zernike moments. Int. Conf. on Pattern Recognition, Rome 1998, pp. 326-328. ### 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

48 features

class (target)nominal10 unique values
0 missing
att1numeric1987 unique values
0 missing
att2numeric1994 unique values
0 missing
att3numeric1994 unique values
0 missing
att4numeric1994 unique values
0 missing
att5numeric1994 unique values
0 missing
att6numeric1994 unique values
0 missing
att7numeric1994 unique values
0 missing
att8numeric1992 unique values
0 missing
att9numeric1994 unique values
0 missing
att10numeric1994 unique values
0 missing
att11numeric1994 unique values
0 missing
att12numeric1994 unique values
0 missing
att13numeric1994 unique values
0 missing
att14numeric1993 unique values
0 missing
att15numeric1994 unique values
0 missing
att16numeric1994 unique values
0 missing
att17numeric1994 unique values
0 missing
att18numeric1994 unique values
0 missing
att19numeric1994 unique values
0 missing
att20numeric1991 unique values
0 missing
att21numeric1994 unique values
0 missing
att22numeric1994 unique values
0 missing
att23numeric1994 unique values
0 missing
att24numeric1994 unique values
0 missing
att25numeric1993 unique values
0 missing
att26numeric1994 unique values
0 missing
att27numeric1994 unique values
0 missing
att28numeric1993 unique values
0 missing
att29numeric1994 unique values
0 missing
att30numeric1994 unique values
0 missing
att31numeric1994 unique values
0 missing
att32numeric1994 unique values
0 missing
att33numeric1994 unique values
0 missing
att34numeric1994 unique values
0 missing
att35numeric1994 unique values
0 missing
att36numeric1994 unique values
0 missing
att37numeric1994 unique values
0 missing
att38numeric1994 unique values
0 missing
att39numeric1994 unique values
0 missing
att40numeric1994 unique values
0 missing
att41numeric1993 unique values
0 missing
att42numeric1994 unique values
0 missing
att43numeric1994 unique values
0 missing
att44numeric1994 unique values
0 missing
att45numeric1994 unique values
0 missing
att46numeric1994 unique values
0 missing
att47numeric1994 unique values
0 missing

107 properties

2000
Number of instances (rows) of the dataset.
48
Number of attributes (columns) of the dataset.
10
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.
47
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.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.65
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
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
0.38
First quartile of skewness among attributes of the numeric type.
0.63
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.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.79
Mean skewness among attributes of the numeric type.
3.76
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
0.31
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
40.13
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.65
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
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
10
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.44
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.32
Entropy of the target attribute values.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
200
Number of instances belonging to the most frequent class.
-0.99
Minimum kurtosis among attributes of the numeric type.
69.88
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.08
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
4.37
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.74
Second quartile (Median) of skewness among attributes of the numeric type.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
508.9
Maximum of means among attributes of the numeric type.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
37.88
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
0.01
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.4
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.
10
The maximum number of distinct values among attributes of the nominal type.
0.07
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1.87
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.79
Maximum skewness among attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.
97.92
Percentage of numeric attributes.
126.83
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
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
124.19
Maximum standard deviation of attributes of the numeric type.
200
Number of instances belonging to the least frequent class.
2.08
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.31
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.25
Third quartile of skewness among attributes of the numeric type.
0.65
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
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.74
Mean kurtosis among attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.46
First quartile of kurtosis among attributes of the numeric type.
65.21
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
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
88.64
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
7.54
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
0.31
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
0.4
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

57 tasks

15683 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
378 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
306 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
305 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
1 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
318 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
170 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
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25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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