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wilt

wilt

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  • OpenML-CC18 study_135 study_98 study_99 study_293 study_270 study_271 study_253 study_285 study_275
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Author: Brian Johnson Source: [UCI] (https://archive.ics.uci.edu/ml/datasets/Wilt) Please cite: Johnson, B., Tateishi, R., Hoan, N., 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20), 6969-6982. __Changes w.r.t. version 1: renamed variables such that they match description.__ ### Dataset: Wilt Data Set ### Abstract: High-resolution Remote Sensing data set (Quickbird). Small number of training samples of diseased trees, large number for other land cover. Testing data set from stratified random sample of image. ### Source: Brian Johnson; Institute for Global Environmental Strategies; 2108-11 Kamiyamaguchi, Hayama, Kanagawa,240-0115 Japan; Email: Johnson '@' iges.or.jp ### Data Set Information: This data set contains some training and testing data from a remote sensing study by Johnson et al. (2013) that involved detecting diseased trees in Quickbird imagery. There are few training samples for the 'diseased trees' class (74) and many for 'other land cover' class (4265). The data set consists of image segments, generated by segmenting the pansharpened image. The segments contain spectral information from the Quickbird multispectral image bands and texture information from the panchromatic (Pan) image band. The testing data set is for the row with “Segmentation scale 15” segments and “original multi-spectral image” Spectral information in Table 2 of the reference (i.e. row 5). Please see the reference below for more information on the data set, and please cite the reference if you use this data set. Enjoy! ### Attribute Information: class: 'w' (diseased trees), 'n' (all other land cover) GLCM_Pan: GLCM mean texture (Pan band) Mean_G: Mean green value Mean_R: Mean red value Mean_NIR: Mean NIR value SD_Pan: Standard deviation (Pan band) ### Relevant Papers: Johnson, B., Tateishi, R., Hoan, N., 2013. A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees. International Journal of Remote Sensing, 34 (20), 6969-6982.

6 features

class (target)nominal2 unique values
0 missing
GLCM_Pannumeric4777 unique values
0 missing
Mean_Gnumeric4234 unique values
0 missing
Mean_Rnumeric4145 unique values
0 missing
Mean_NIRnumeric4646 unique values
0 missing
SD_Plannumeric4802 unique values
0 missing
Show all 6 features

62 properties

NumberOfInstances
4839
Number of instances (rows) of the dataset.
NumberOfFeatures
6
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
5
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
16.67
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
83.33
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
16.67
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.75
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
70.39
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
-0.28
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
12.09
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
17.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
126.86
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
2.5
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
62.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
107.68
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
378.61
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
7.46
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
109.92
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
0.97
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
204.97
Mean of means among attributes of the numeric type.
ClassEntropy
0.3
Entropy of the target attribute values.
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.
MajorityClassPercentage
94.61
Percentage of instances belonging to the most frequent class.
MajorityClassSize
4578
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
119.74
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
525.8
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
7.73
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
156.58
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
48.01
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.37
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
61.21
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.02
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
24.48
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.75
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
10.73
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
5.39
Percentage of instances belonging to the least frequent class.
MinorityClassSize
261
Number of instances belonging to the least frequent class.
Show all 62 properties

22 tasks

Supervised Classification on wilt
10966 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on wilt
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on wilt
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
Supervised Classification on wilt
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on wilt
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on wilt
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on wilt
0 runs
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
Clustering on wilt
0 runs - estimation_procedure: 50 times Clustering
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