Data
segment

segment

active ARFF Publicly available Visibility: public Uploaded 04-12-2017 by Jann Goschenhofer
0 likes downloaded by 2 people , 2 total downloads 0 issues 0 downvotes
  • OpenML-CC18 study_135 study_98 study_99
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. __Major changes w.r.t. version 2: ignored first two variables as they do not fit the classification task (they reflect the location of the sample in the original image). The 3rd is constant, so should also be ignored.__ ### Attribute Information 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.col (ignore)numeric253 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

62 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
Percentage of binary attributes.
15.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.75
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
339.22
Maximum kurtosis among attributes of the numeric type.
-12.69
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
35.91
Third quartile of kurtosis among attributes of the numeric type.
123.42
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
95
Percentage of numeric attributes.
34.87
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
7
The minimal number of distinct values among attributes of the nominal type.
5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
7
The maximum number of distinct values among attributes of the nominal type.
-0.89
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.41
Third quartile of skewness among attributes of the numeric type.
16.9
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0.14
First quartile of kurtosis among attributes of the numeric type.
43.07
Third quartile of standard deviation of attributes of the numeric type.
58.81
Maximum standard deviation of attributes of the numeric type.
14.29
Percentage of instances belonging to the least frequent class.
0.01
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
330
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
40.85
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.86
First quartile of skewness among attributes of the numeric type.
19.06
Mean of means among attributes of the numeric type.
1.22
First quartile of standard deviation of attributes of the numeric type.
0.15
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.81
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.81
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
7
Average number of distinct values among the attributes of the nominal type.
6.98
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.51
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
14.29
Percentage of instances belonging to the most frequent class.
22.67
Mean standard deviation of attributes of the numeric type.
1.33
Second quartile (Median) of skewness among attributes of the numeric type.
330
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

9 tasks

4249 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - 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
Define a new task