Data
segment

segment

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: Image Segmentation data 2. Source Information -- Creators: Vision Group, University of Massachusetts -- Donor: Vision Group (Carla Brodley, brodley@cs.umass.edu) -- Date: November, 1990 3. Past Usage: None yet published 4. Relevant Information: The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region. 5. Number of Instances: Training data: 210 Test data: 2100 6. Number of Attributes: 19 continuous attributes 7. 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) 8. Missing Attribute Values: None 9. Class Distribution: Classes: brickface, sky, foliage, cement, window, path, grass. 30 instances per class for training data. 300 instances per class for test data. Relabeled values in attribute class From: 1 To: brickface From: 2 To: sky From: 3 To: foliage From: 4 To: cement From: 5 To: window From: 6 To: path From: 7 To: grass

20 features

class (target)nominal7 unique values
0 missing
rawred-meannumeric681 unique values
0 missing
hue-meannumeric1922 unique values
0 missing
saturation-meannumeric1899 unique values
0 missing
value-meannumeric785 unique values
0 missing
exgreen-meannumeric377 unique values
0 missing
exblue-meannumeric636 unique values
0 missing
exred-meannumeric430 unique values
0 missing
rawgreen-meannumeric691 unique values
0 missing
rawblue-meannumeric781 unique values
0 missing
region-centroid-colnumeric253 unique values
0 missing
intensity-meannumeric1271 unique values
0 missing
hedge-sdnumeric1180 unique values
0 missing
hedge-meannumeric262 unique values
0 missing
vegde-sdnumeric1082 unique values
0 missing
vedge-meannumeric234 unique values
0 missing
short-line-density-2numeric3 unique values
0 missing
short-line-density-5numeric4 unique values
0 missing
region-pixel-countnumeric1 unique values
0 missing
region-centroid-rownumeric238 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.
0.15
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
0.04
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
0.95
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
0.98
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
0.04
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
0.95
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
0.98
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
0.04
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
0.95
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
2.81
Entropy of the target attribute values.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.72
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.94
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
0.05
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
14.29
Percentage of instances belonging to the most frequent class.
330
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
339.22
Maximum kurtosis among attributes of the numeric type.
124.91
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
16.9
Maximum skewness among attributes of the numeric type.
72.96
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
38.52
Mean kurtosis among attributes of the numeric type.
24.63
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.
7
Average number of distinct values among the attributes of the nominal type.
3.32
Mean skewness among attributes of the numeric type.
25.31
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.22
Minimum kurtosis among attributes of the numeric type.
-12.69
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
-0.89
Minimum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
14.29
Percentage of instances belonging to the least frequent class.
330
Number of instances belonging to the least frequent class.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.77
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.
95
Percentage of numeric attributes.
5
Percentage of nominal attributes.
First quartile of entropy among attributes.
0.09
First quartile of kurtosis among attributes of the numeric type.
0.01
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
0.69
First quartile of skewness among attributes of the numeric type.
1.55
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.69
Second quartile (Median) of kurtosis among attributes of the numeric type.
8.24
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.
1.3
Second quartile (Median) of skewness among attributes of the numeric type.
19.57
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
34.34
Third quartile of kurtosis among attributes of the numeric type.
37.05
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
5.37
Third quartile of skewness 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.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
0.98
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
0.98
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
0.06
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
0.06
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
0.06
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.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.94
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
Define a new task