Data
page-blocks

page-blocks

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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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

19 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
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.87
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
90.91
Percentage of numeric attributes.
89.77
Percentage of instances belonging to the most frequent class.
9.09
Percentage of nominal attributes.
4913
Number of instances belonging to the most frequent class.
0.51
Percentage of instances belonging to the least frequent class.
28
Number of instances belonging to the least frequent class.

13 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
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
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