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volcanoes-e2

volcanoes-e2

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Author: Michael C. Burl Source: UCI Please cite: * Dataset Title: Volcanoes on Venus - JARtool experiment Data Set Experiment: E2 * Source: Michael C. Burl MS 126-347, JPL 4800 Oak Grove Drive Pasadena, CA 91109 (818) 393-5345 Michael.C.Burl '@' jpl.nasa.gov http://www-aig.jpl.nasa.gov/mls/home/burl/ * Data Set Information: The data was collected by the Magellan spacecraft over an approximately four year period from 1990--1994. The objective of the mission was to obtain global mapping of the surface of Venus using synthetic aperture radar (SAR). A more detailed discussion of the mission and objectives is available at JPL's Magellan webpage. There are some spatial dependencies. For example, background patches from with in a single image are likely to be more similar than background patches taken across different images. In addition to the images, there are "ground truth" files that specify the locations of volcanoes within the images. The quotes around "ground truth" are intended as a reminder that there is no absolute ground truth for this data set. No one has been to Venus and the image quality does not permit 100%, unambiguous identification of the volcanoes, even by human experts. There are labels that provide some measure of subjective uncertainty (1 = definitely a volcano, 2 = probably, 3 = possibly, 4 = only a pit is visible). See reference [Smyth95] for more information on the labeling uncertainty problem. There are also files that specify the exact set of experiments using in the published evaluations of the JARtool system. * Attribute Information: The images are 1024X1024 pixels. The pixel values are in the range [0,255]. The pixel value is related to the amount of energy backscattered to the radar from a given spatial location. Higher pixel values indicate greater backscatter. Lower pixel values indicate lesser backscatter. Both topography and surface roughness relative to the radar wavelength affect the amount of backscatter. * Relevant Papers: G.H. Pettengill, P.G. Ford, W.T.K. Johnson, R.K. Raney, L.A. Soderblom, "Magellan: Radar Performance and Data Products", Science, 252:260-265 (1991). R.S. Saunders, A.J. Spear, P.C. Allin, R.S. Austin, A.L. Berman, R.C. Chandlee, J. Clark, A.V. Decharon, E.M. Dejong, "Magellan Mission Summary", J. of Geophysical Research Planets, 97(E8):13067-13090, (1992). M.C. Burl, L. Asker, P. Smyth, U. Fayyad, P. Perona, L. Crumpler, and J. Aubele, "Learning to Recognize Volcanoes on Venus", Machine Learning, (March 1998). P. Smyth, M.C. Burl, U.M. Fayyad, and P. Perona, Chapter: "Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth", In Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Menlo Park, CA, (1995).

4 features

Class (target)nominal5 unique values
0 missing
V1numeric645 unique values
0 missing
V2numeric639 unique values
0 missing
V3numeric1077 unique values
0 missing
Show all 4 features

107 properties

NumberOfInstances
1080
Number of instances (rows) of the dataset.
NumberOfFeatures
4
Number of attributes (columns) of the dataset.
NumberOfClasses
5
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
3
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.95
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.5
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
CfsSubsetEval_DecisionStumpErrRate
0.09
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
CfsSubsetEval_DecisionStumpKappa
0
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
CfsSubsetEval_NaiveBayesAUC
0.5
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
CfsSubsetEval_NaiveBayesErrRate
0.09
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
CfsSubsetEval_NaiveBayesKappa
0
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
CfsSubsetEval_kNN1NAUC
0.5
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
CfsSubsetEval_kNN1NErrRate
0.09
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
CfsSubsetEval_kNN1NKappa
0
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
ClassEntropy
0.58
Entropy of the target attribute values.
DecisionStumpAUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
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.
J48.00001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
91.11
Percentage of instances belonging to the most frequent class.
MajorityClassSize
984
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.81
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
539.68
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
5
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.14
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
285.39
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.51
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
341.81
Mean of means among attributes of the numeric type.
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
5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.36
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
189.57
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.2
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.45
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
5
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.14
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.09
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.74
Percentage of instances belonging to the least frequent class.
MinorityClassSize
8
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
75
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
25
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.2
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.45
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.14
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.09
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.15
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
485.31
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
0.08
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
283.23
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.81
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
539.68
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
1.14
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
285.39
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on volcanoes-e2
74 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on volcanoes-e2
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on volcanoes-e2
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Clustering on volcanoes-e2
0 runs
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
Clustering on volcanoes-e2
0 runs - estimation_procedure: 50 times Clustering
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