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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
Show all 11 features

107 properties

NumberOfInstances
5473
Number of instances (rows) of the dataset.
NumberOfFeatures
11
Number of attributes (columns) of the dataset.
NumberOfClasses
5
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
10
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.87
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.64
Entropy of the target attribute values.
DecisionStumpAUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.58
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.83
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
89.77
Percentage of instances belonging to the most frequent class.
MajorityClassSize
4913
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
4816.89
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1198.41
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
5
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
67.43
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
4849.38
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
652.83
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
253.33
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
14.71
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
840.23
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
0.69
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.37
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
5
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.83
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.17
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.51
Percentage of instances belonging to the least frequent class.
MinorityClassSize
28
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.59
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
90.91
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
9.09
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.99
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
4.86
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.99
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
14.26
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
123.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
51.66
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
8.93
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
91.9
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
528.48
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
459.73
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
19.74
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1423.13
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

27 tasks

Supervised Classification on page-blocks
1403 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on page-blocks
324 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on page-blocks
313 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on page-blocks
161 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on page-blocks
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Learning Curve on page-blocks
292 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on page-blocks
170 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on page-blocks
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on page-blocks
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on page-blocks
0 runs
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
Clustering on page-blocks
0 runs - estimation_procedure: 50 times Clustering
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