Data
segment

segment

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 22 people , 24 total downloads 0 issues 0 downvotes
  • OpenML100 study_1 study_123 study_14 study_34 study_37 study_41 study_50 study_52 study_7 study_70 study_76 uci
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: University of Massachusetts Vision Group, Carla Brodley Source: [UCI](http://archive.ics.uci.edu/ml/datasets/image+segmentation) - 1990 Please cite: [UCI](http://archive.ics.uci.edu/ml/citation_policy.html) Image Segmentation Data Set The instances were drawn randomly from a database of 7 outdoor images. The images were hand-segmented to create a classification for every pixel. Each instance is a 3x3 region. ### Attribute Information 1. region-centroid-col: the column of the center pixel of the region. 2. region-centroid-row: the row of the center pixel of the region. 3. region-pixel-count: the number of pixels in a region = 9. 4. short-line-density-5: the results of a line extractoin algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region. 5. short-line-density-2: same as short-line-density-5 but counts lines of high contrast, greater than 5. 6. vedge-mean: measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector. 7. vegde-sd: (see 6) 8. hedge-mean: measures the contrast of vertically adjacent pixels. Used for horizontal line detection. 9. hedge-sd: (see 8). 10. intensity-mean: the average over the region of (R + G + B)/3 11. rawred-mean: the average over the region of the R value. 12. rawblue-mean: the average over the region of the B value. 13. rawgreen-mean: the average over the region of the G value. 14. exred-mean: measure the excess red: (2R - (G + B)) 15. exblue-mean: measure the excess blue: (2B - (G + R)) 16. exgreen-mean: measure the excess green: (2G - (R + B)) 17. value-mean: 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics) 18. saturatoin-mean: (see 17) 19. hue-mean: (see 17)

20 features

class (target)nominal7 unique values
0 missing
region-centroid-colnumeric253 unique values
0 missing
region-centroid-rownumeric238 unique values
0 missing
region-pixel-countnumeric1 unique values
0 missing
short-line-density-5numeric4 unique values
0 missing
short-line-density-2numeric3 unique values
0 missing
vedge-meannumeric234 unique values
0 missing
vegde-sdnumeric1082 unique values
0 missing
hedge-meannumeric262 unique values
0 missing
hedge-sdnumeric1180 unique values
0 missing
intensity-meannumeric1271 unique values
0 missing
rawred-meannumeric681 unique values
0 missing
rawblue-meannumeric781 unique values
0 missing
rawgreen-meannumeric691 unique values
0 missing
exred-meannumeric430 unique values
0 missing
exblue-meannumeric636 unique values
0 missing
exgreen-meannumeric377 unique values
0 missing
value-meannumeric785 unique values
0 missing
saturation-meannumeric1899 unique values
0 missing
hue-meannumeric1922 unique values
0 missing

107 properties

2310
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
7
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.
19
Number of numeric attributes.
1
Number of nominal attributes.
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.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.95
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
7
Average number of distinct values among the attributes of the nominal type.
0.69
First quartile of skewness among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.32
Mean skewness among attributes of the numeric type.
1.55
First quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.04
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.31
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
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
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
14.29
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.81
Entropy of the target attribute values.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
330
Number of instances belonging to the most frequent class.
-1.22
Minimum kurtosis among attributes of the numeric type.
8.24
Second quartile (Median) of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-12.69
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
339.22
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.3
Second quartile (Median) of skewness among attributes of the numeric type.
0.92
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
124.91
Maximum of means among attributes of the numeric type.
7
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
19.57
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.01
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.89
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.06
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.
7
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
34.34
Third quartile of kurtosis among attributes of the numeric type.
0.15
Average class difference between consecutive instances.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
16.9
Maximum skewness among attributes of the numeric type.
14.29
Percentage of instances belonging to the least frequent class.
95
Percentage of numeric attributes.
37.05
Third quartile of means among attributes of the numeric type.
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
0.97
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.J48 -C .00001
72.96
Maximum standard deviation of attributes of the numeric type.
330
Number of instances belonging to the least frequent class.
5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.04
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
5.37
Third quartile of skewness among attributes of the numeric type.
0.95
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
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
38.52
Mean kurtosis among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.09
First quartile of kurtosis among attributes of the numeric type.
43.53
Third quartile of standard deviation of attributes of the numeric type.
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
0.97
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.J48 -C .0001
24.63
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.04
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

44 tasks

12318 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
322 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
311 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
184 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
318 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
171 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
26 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
16 runs - target_feature: class
1310 runs - target_feature: class
1309 runs - target_feature: class
1309 runs - target_feature: class
1308 runs - target_feature: class
1307 runs - target_feature: class
1303 runs - target_feature: class
1300 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
Define a new task