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pendigits

pendigits

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Author: Source: Unknown - Please cite: 1. Title of Database: Pen-Based Recognition of Handwritten Digits 2. Source: E. Alpaydin, F. Alimoglu Department of Computer Engineering Bogazici University, 80815 Istanbul Turkey alpaydin@boun.edu.tr July 1998 3. Past Usage: F. Alimoglu (1996) Combining Multiple Classifiers for Pen-Based Handwritten Digit Recognition, MSc Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University. http://www.cmpe.boun.edu.tr/~alimoglu/alimoglu.ps.gz F. Alimoglu, E. Alpaydin, "Methods of Combining Multiple Classifiers Based on Different Representations for Pen-based Handwriting Recognition," Proceedings of the Fifth Turkish Artificial Intelligence and Artificial Neural Networks Symposium (TAINN 96), June 1996, Istanbul, Turkey. http://www.cmpe.boun.edu.tr/~alimoglu/tainn96.ps.gz 4. Relevant Information: We create a digit database by collecting 250 samples from 44 writers. The samples written by 30 writers are used for training, cross-validation and writer dependent testing, and the digits written by the other 14 are used for writer independent testing. This database is also available in the UNIPEN format. We use a WACOM PL-100V pressure sensitive tablet with an integrated LCD display and a cordless stylus. The input and display areas are located in the same place. Attached to the serial port of an Intel 486 based PC, it allows us to collect handwriting samples. The tablet sends $x$ and $y$ tablet coordinates and pressure level values of the pen at fixed time intervals (sampling rate) of 100 miliseconds. These writers are asked to write 250 digits in random order inside boxes of 500 by 500 tablet pixel resolution. Subject are monitored only during the first entry screens. Each screen contains five boxes with the digits to be written displayed above. Subjects are told to write only inside these boxes. If they make a mistake or are unhappy with their writing, they are instructed to clear the content of a box by using an on-screen button. The first ten digits are ignored because most writers are not familiar with this type of input devices, but subjects are not aware of this. In our study, we use only ($x, y$) coordinate information. The stylus pressure level values are ignored. First we apply normalization to make our representation invariant to translations and scale distortions. The raw data that we capture from the tablet consist of integer values between 0 and 500 (tablet input box resolution). The new coordinates are such that the coordinate which has the maximum range varies between 0 and 100. Usually $x$ stays in this range, since most characters are taller than they are wide. In order to train and test our classifiers, we need to represent digits as constant length feature vectors. A commonly used technique leading to good results is resampling the ( x_t, y_t) points. Temporal resampling (points regularly spaced in time) or spatial resampling (points regularly spaced in arc length) can be used here. Raw point data are already regularly spaced in time but the distance between them is variable. Previous research showed that spatial resampling to obtain a constant number of regularly spaced points on the trajectory yields much better performance, because it provides a better alignment between points. Our resampling algorithm uses simple linear interpolation between pairs of points. The resampled digits are represented as a sequence of T points ( x_t, y_t )_{t=1}^T, regularly spaced in arc length, as opposed to the input sequence, which is regularly spaced in time. So, the input vector size is 2*T, two times the number of points resampled. We considered spatial resampling to T=8,12,16 points in our experiments and found that T=8 gave the best trade-off between accuracy and complexity. 5. Number of Instances pendigits.tra Training 7494 pendigits.tes Testing 3498 The way we used the dataset was to use first half of training for actual training, one-fourth for validation and one-fourth for writer-dependent testing. The test set was used for writer-independent testing and is the actual quality measure. 6. Number of Attributes 16 input+1 class attribute 7. For Each Attribute: All input attributes are integers in the range 0..100. The last attribute is the class code 0..9 8. Missing Attribute Values None 9. Class Distribution Class: No of examples in training set 0: 780 1: 779 2: 780 3: 719 4: 780 5: 720 6: 720 7: 778 8: 719 9: 719 Class: No of examples in testing set 0: 363 1: 364 2: 364 3: 336 4: 364 5: 335 6: 336 7: 364 8: 336 9: 336 Accuracy on the testing set with k-nn using Euclidean distance as the metric k = 1 : 97.74 k = 2 : 97.37 k = 3 : 97.80 k = 4 : 97.66 k = 5 : 97.60 k = 6 : 97.57 k = 7 : 97.54 k = 8 : 97.54 k = 9 : 97.46 k = 10 : 97.48 k = 11 : 97.34

17 features

class (target)nominal10 unique values
0 missing
input9numeric101 unique values
0 missing
input16numeric101 unique values
0 missing
input15numeric101 unique values
0 missing
input14numeric101 unique values
0 missing
input13numeric101 unique values
0 missing
input12numeric101 unique values
0 missing
input11numeric101 unique values
0 missing
input10numeric101 unique values
0 missing
input1numeric101 unique values
0 missing
input8numeric101 unique values
0 missing
input7numeric101 unique values
0 missing
input6numeric101 unique values
0 missing
input5numeric101 unique values
0 missing
input4numeric98 unique values
0 missing
input3numeric101 unique values
0 missing
input2numeric96 unique values
0 missing

107 properties

10992
Number of instances (rows) of the dataset.
17
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.
16
Number of numeric attributes.
1
Number of nominal attributes.
0.1
Average class difference between consecutive instances.
0.98
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
0.05
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
0.94
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
0.98
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
0.05
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
0.94
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
0.98
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
0.05
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
0.94
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
3.32
Entropy of the target attribute values.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.04
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.41
Percentage of instances belonging to the most frequent class.
1144
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
3.05
Maximum kurtosis among attributes of the numeric type.
85.12
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
0.95
Maximum skewness among attributes of the numeric type.
41.76
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
-0.5
Mean kurtosis among attributes of the numeric type.
50.71
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
10
Average number of distinct values among the attributes of the nominal type.
-0.04
Mean skewness among attributes of the numeric type.
29.77
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.69
Minimum kurtosis among attributes of the numeric type.
28.85
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
-1.49
Minimum skewness among attributes of the numeric type.
16.22
Minimum standard deviation of attributes of the numeric type.
9.6
Percentage of instances belonging to the least frequent class.
1055
Number of instances belonging to the least frequent class.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.14
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
94.12
Percentage of numeric attributes.
5.88
Percentage of nominal attributes.
First quartile of entropy among attributes.
-1.2
First quartile of kurtosis among attributes of the numeric type.
35.91
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.43
First quartile of skewness among attributes of the numeric type.
26.51
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
-0.78
Second quartile (Median) of kurtosis among attributes of the numeric type.
48.53
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.09
Second quartile (Median) of skewness among attributes of the numeric type.
30.24
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-0.26
Third quartile of kurtosis among attributes of the numeric type.
59.6
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.45
Third quartile of skewness among attributes of the numeric type.
34.23
Third quartile of standard deviation of attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.05
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - 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: 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: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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