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mfeat-morphological

mfeat-morphological

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Author: Source: Unknown - Please cite: The multi-feature digit dataset ------------------------------- Oowned and donated by: ---------------------- Robert P.W. Duin Department of Applied Physics Delft University of Technology P.O. Box 5046, 2600 GA Delft The Netherlands email: duin@ph.tn.tudelft.nl http : //www.ph.tn.tudelft.nl/~duin tel +31 15 2786143 Usage ----- 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. M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), The database as it is is used in: A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, in preparation Description ----------- This dataset consists of features of handwritten numerals (0 - 9) extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images. These digits are represented in terms of the following six feature sets (files): 1. mfeat-fou: 76 Fourier coefficients of the character shapes; 2. mfeat-fac: 216 profile correlations; 3. mfeat-kar: 64 Karhunen-Love coefficients; 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; 5. mfeat-zer: 47 Zernike moments; 6. mfeat-mor: 6 morphological features. In each file the 2000 patterns are stored in ASCI on 2000 lines. The first 200 patterns are of class '0', followed by sets of 200 patterns for each of the classes '1' - '9'. Corresponding patterns in different feature sets (files) correspond to the same original character. The source image dataset is lost. Using the pixel-dataset (mfeat-pix) sampled versions of the original images may be obtained (15 x 16 pixels). Total number of instances: -------------------------- 2000 (200 instances per class) Total number of attributes: --------------------------- 649 (distributed over 6 datasets,see above) no missing attributes Total number of classes: ------------------------ 10 Format: ------ 6 files, see above. Each file contains 2000 lines, one for each instance. Attributes are SPACE separated and can be loaded by Matlab as > load filename No missing attributes. Some are integer, others are real. Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

7 features

class (target)nominal10 unique values
0 missing
att1numeric3 unique values
0 missing
att2numeric7 unique values
0 missing
att3numeric6 unique values
0 missing
att4numeric1717 unique values
0 missing
att5numeric1886 unique values
0 missing
att6numeric1888 unique values
0 missing

107 properties

2000
Number of instances (rows) of the dataset.
7
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.
6
Number of numeric attributes.
1
Number of nominal attributes.
1656.11
Third quartile of means among attributes of the numeric type.
0.91
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3757.63
Maximum standard deviation of attributes of the numeric type.
10
Percentage of instances belonging to the least frequent class.
85.71
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.3
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
200
Number of instances belonging to the least frequent class.
14.29
Percentage of nominal attributes.
0.77
Third quartile of skewness among attributes of the numeric type.
0.67
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
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.54
Mean kurtosis among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
958.17
Third quartile of standard deviation of attributes of the numeric type.
0.91
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1052.7
Mean of means among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.87
First quartile of kurtosis among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.3
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
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.66
First quartile of means among attributes of the numeric type.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
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
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
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.
0.21
First quartile of skewness among attributes of the numeric type.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.91
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.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
0.57
First quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.3
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.53
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
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
0.33
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
10
Percentage of instances belonging to the most frequent class.
630.91
Mean standard deviation of attributes of the numeric type.
-0.64
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.32
Entropy of the target attribute values.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
200
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.69
Second quartile (Median) of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.08
Maximum kurtosis among attributes of the numeric type.
0.49
Minimum of means among attributes of the numeric type.
0.62
Second quartile (Median) of skewness among attributes of the numeric type.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
6155.2
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.92
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
Third quartile of entropy among attributes.
0.33
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.
-0.06
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.11
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.01
Maximum skewness among attributes of the numeric type.
0.29
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.

11 tasks

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