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page-blocks

page-blocks

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Author: Source: Unknown - Please cite: 1. Title of Database: Blocks Classification 2. Sources: (a) Donato Malerba Dipartimento di Informatica University of Bari via Orabona 4 70126 Bari - Italy phone: +39 - 80 - 5443269 fax: +39 - 80 - 5443196 malerbad@vm.csata.it (b) Donor: Donato Malerba (c) Date: July 1995 3. Past Usage: This data set have been used to try different simplification methods for decision trees. A summary of the results can be found in: Malerba, D., Esposito, F., and Semeraro, G. "A Further Comparison of Simplification Methods for Decision-Tree Induction." In D. Fisher and H. Lenz (Eds.), "Learning from Data: Artificial Intelligence and Statistics V", Lecture Notes in Statistics, Springer Verlag, Berlin, 1995. The problem consists in classifying all the blocks of the page layout of a document that has been detected by a segmentation process. This is an essential step in document analysis in order to separate text from graphic areas. Indeed, the five classes are: text (1), horizontal line (2), picture (3), vertical line (4) and graphic (5). For a detailed presentation of the problem see: Esposito F., Malerba D., & Semeraro G. Multistrategy Learning for Document Recognition Applied Artificial Intelligence, 8, pp. 33-84, 1994 All instances have been personally checked so that low noise is present in the data. 4. Relevant Information Paragraph: The 5473 examples comes from 54 distinct documents. Each observation concerns one block. All attributes are numeric. Data are in a format readable by C4.5. 5. Number of Instances: 5473. 6. Number of Attributes height: integer. | Height of the block. lenght: integer. | Length of the block. area: integer. | Area of the block (height * lenght); eccen: continuous. | Eccentricity of the block (lenght / height); p_black: continuous. | Percentage of black pixels within the block (blackpix / area); p_and: continuous. | Percentage of black pixels after the application of the Run Length Smoothing Algorithm (RLSA) (blackand / area); mean_tr: continuous. | Mean number of white-black transitions (blackpix / wb_trans); blackpix: integer. | Total number of black pixels in the original bitmap of the block. blackand: integer. | Total number of black pixels in the bitmap of the block after the RLSA. wb_trans: integer. | Number of white-black transitions in the original bitmap of the block. 7. Missing Attribute Values: No missing value. 8. Class Distribution: Valid Cum Class Frequency Percent Percent Percent text 4913 89.8 89.8 89.8 horiz. line 329 6.0 6.0 95.8 graphic 28 .5 .5 96.3 vert. line 88 1.6 1.6 97.9 picture 115 2.1 2.1 100.0 ------- ------- ------- TOTAL 5473 100.0 100.0 Summary Statistics: Variable Mean Std Dev Minimum Maximum Correlation HEIGHT 10.47 18.96 1 804 .3510 LENGTH 89.57 114.72 1 553 -.0045 AREA 1198.41 4849.38 7 143993 .2343 ECCEN 13.75 30.70 .007 537.00 .0992 P_BLACK .37 .18 .052 1.00 .2130 P_AND .79 .17 .062 1.00 -.1771 MEAN_TR 6.22 69.08 1.00 4955.00 .0723 BLACKPIX 365.93 1270.33 7 33017 .1656 BLACKAND 741.11 1881.50 7 46133 .1565 WB_TRANS 106.66 167.31 1 3212 .0337 Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

11 features

class (target)nominal5 unique values
0 missing
heightnumeric104 unique values
0 missing
lenghtnumeric452 unique values
0 missing
areanumeric1395 unique values
0 missing
eccennumeric1511 unique values
0 missing
p_blacknumeric711 unique values
0 missing
p_andnumeric700 unique values
0 missing
mean_trnumeric851 unique values
0 missing
blackpixnumeric1069 unique values
0 missing
blackandnumeric1718 unique values
0 missing
wb_transnumeric581 unique values
0 missing

107 properties

5473
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
5
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.
10
Number of numeric attributes.
1
Number of nominal attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
253.33
Mean of means among attributes of the numeric type.
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.99
First quartile of kurtosis among attributes of the numeric type.
1423.13
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.86
First quartile of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.92
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.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
5
Average number of distinct values among the attributes of the nominal type.
1.99
First quartile of skewness among attributes of the numeric type.
0.81
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.71
Mean skewness among attributes of the numeric type.
14.26
First quartile of standard deviation of attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
89.77
Percentage of instances belonging to the most frequent class.
840.23
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.83
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
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4913
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
123.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.64
Entropy of the target attribute values.
Maximum entropy among attributes.
0.69
Minimum kurtosis among attributes of the numeric type.
51.66
Second quartile (Median) of means among attributes of the numeric type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4816.89
Maximum kurtosis among attributes of the numeric type.
0.37
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1198.41
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
8.93
Second quartile (Median) of skewness among attributes of the numeric type.
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
91.9
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.89
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.
5
The maximum number of distinct values among attributes of the nominal type.
-0.83
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.04
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.
67.43
Maximum skewness among attributes of the numeric type.
0.17
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
528.48
Third quartile of kurtosis among attributes of the numeric type.
0.87
Average class difference between consecutive instances.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4849.38
Maximum standard deviation of attributes of the numeric type.
0.51
Percentage of instances belonging to the least frequent class.
90.91
Percentage of numeric attributes.
459.73
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
28
Number of instances belonging to the least frequent class.
9.09
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.03
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
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
652.83
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.
19.74
Third quartile of skewness among attributes of the numeric type.
0.83
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

14 tasks

1419 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
325 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
314 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
162 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
48 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
290 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
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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