Data
wilt

wilt

active ARFF Publicly available Visibility: public Uploaded 04-12-2017 by Jann Goschenhofer
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  • OpenML-CC18 study_135 study_98 study_99
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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

62 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.
-0.28
First quartile of skewness among attributes of the numeric type.
204.97
Mean of means among attributes of the numeric type.
12.09
First quartile of standard deviation of attributes of the numeric type.
0.97
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.3
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.
17.19
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
126.86
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.
3.37
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
94.61
Percentage of instances belonging to the most frequent class.
61.21
Mean standard deviation of attributes of the numeric type.
2.5
Second quartile (Median) of skewness among attributes of the numeric type.
4578
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
16.67
Percentage of binary attributes.
62.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.02
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
119.74
Maximum kurtosis among attributes of the numeric type.
24.48
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
107.68
Third quartile of kurtosis among attributes of the numeric type.
525.8
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
83.33
Percentage of numeric attributes.
378.61
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.75
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
7.46
Third quartile of skewness among attributes of the numeric type.
7.73
Maximum skewness among attributes of the numeric type.
10.73
Minimum standard deviation of attributes of the numeric type.
3.75
First quartile of kurtosis among attributes of the numeric type.
109.92
Third quartile of standard deviation of attributes of the numeric type.
156.58
Maximum 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.
70.39
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.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
48.01
Mean kurtosis among attributes of the numeric type.

11 tasks

8036 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - 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
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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