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  • OpenML-CC18 study_135 study_218 study_98 study_99 study_271 study_240 study_253 study_275
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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
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.col (ignore)numeric253 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
Show all 20 features

62 properties

NumberOfInstances
2310
Number of instances (rows) of the dataset.
NumberOfFeatures
20
Number of attributes (columns) of the dataset.
NumberOfClasses
7
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
19
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
95
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
5
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.14
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.01
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.86
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.22
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.81
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
6.98
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
1.33
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
15.58
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
35.91
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
34.87
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
5.41
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
43.07
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.15
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
19.06
Mean of means among attributes of the numeric type.
ClassEntropy
2.81
Entropy of the target attribute values.
Dimensionality
0.01
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.
MajorityClassPercentage
14.29
Percentage of instances belonging to the most frequent class.
MajorityClassSize
330
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
339.22
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
123.42
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
7
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
16.9
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
58.81
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
40.85
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
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
7
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.51
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
22.67
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.75
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-12.69
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
7
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.89
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
14.29
Percentage of instances belonging to the least frequent class.
MinorityClassSize
330
Number of instances belonging to the least frequent class.
Show all 62 properties

26 tasks

Supervised Classification on segment
9967 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on segment
4 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on segment
2 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
Supervised Classification on segment
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on segment
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on segment
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: class
Supervised Classification on segment
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on segment
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on segment
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on segment
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on segment
0 runs
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
Clustering on segment
0 runs - estimation_procedure: 50 times Clustering
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