Data
wilt

wilt

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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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. * 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
V1numeric4777 unique values
0 missing
V2numeric4234 unique values
0 missing
V3numeric4145 unique values
0 missing
V4numeric4646 unique values
0 missing
V5numeric4802 unique values
0 missing

107 properties

4839
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
2
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.
5
Number of numeric attributes.
1
Number of nominal attributes.
0.97
Average class difference between consecutive instances.
0.93
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
0.02
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
0.8
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
0.93
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
0.02
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
0.8
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
0.93
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
0.02
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
0.8
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
0.3
Entropy of the target attribute values.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.05
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
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.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.02
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
94.61
Percentage of instances belonging to the most frequent class.
4578
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
119.74
Maximum kurtosis among attributes of the numeric type.
525.8
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
7.73
Maximum skewness among attributes of the numeric type.
156.58
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
48.01
Mean kurtosis among attributes of the numeric type.
204.97
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.
2
Average number of distinct values among the attributes of the nominal type.
3.37
Mean skewness among attributes of the numeric type.
61.21
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.02
Minimum kurtosis among attributes of the numeric type.
24.48
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-0.75
Minimum skewness among attributes of the numeric type.
10.73
Minimum standard deviation of attributes of the numeric type.
5.39
Percentage of instances belonging to the least frequent class.
261
Number of instances belonging to the least frequent class.
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
16.67
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
83.33
Percentage of numeric attributes.
16.67
Percentage of nominal attributes.
First quartile of entropy among attributes.
3.75
First quartile of kurtosis among attributes of the numeric type.
70.39
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.28
First quartile of skewness among attributes of the numeric type.
12.09
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
17.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
126.86
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.
2.5
Second quartile (Median) of skewness among attributes of the numeric type.
62.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
107.68
Third quartile of kurtosis among attributes of the numeric type.
378.61
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
7.46
Third quartile of skewness among attributes of the numeric type.
109.92
Third quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.02
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.75
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.
0.65
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

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 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: 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
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