one-hundred-plants-margin
| Issue |
#Downvotes for this reason |
By |
|
Author: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set) - 2010
Please cite: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.
### Description
One-hundred plant species leaves dataset (Class = Margin).
### Sources
```
(a) Original owners of colour Leaves Samples:
James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
The colour images are not included.
The Leaves were collected in the Royal Botanic Gardens, Kew, UK.
email: james.cope@kingston.ac.uk
(b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.
Donor of database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk
```
### Dataset Information
The original data directory contains the binary images (masks) of the leaf samples (colour images not included).
There are three features for each image: Shape, Margin and Texture.
For each feature, a 64 element vector is given per leaf sample.
These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin).
So, there are three different files, one for each feature problem:
* 'data_Sha_64.txt' -> prediction based on shape
* 'data_Tex_64.txt' -> prediction based on texture
* 'data_Mar_64.txt' -> prediction based on margin [dataset provided here]
Each row has a 64-element feature vector followed by the Class label.
There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.
### Attributes Information
Three 64 element feature vectors per sample.
### Relevant Papers
Charles Mallah, James Cope, James Orwell.
Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features.
Signal Processing, Pattern Recognition and Applications, in press.
J. Cope, P. Remagnino, S. Barman, and P. Wilkin.
Plant texture classification using gabor co-occurrences.
Advances in Visual Computing,
pages 699-677, 2010.
T. Beghin, J. Cope, P. Remagnino, and S. Barman.
Shape and texture based plant leaf classification.
In: Advanced Concepts for Intelligent Vision Systems,
pages 345-353. Springer, 2010.
65 features
| Class (target) | nominal | 100 unique values
0 missing | |
| V1 | numeric | 46 unique values
0 missing | |
| V2 | numeric | 90 unique values
0 missing | |
| V3 | numeric | 69 unique values
0 missing | |
| V4 | numeric | 78 unique values
0 missing | |
| V5 | numeric | 53 unique values
0 missing | |
| V6 | numeric | 121 unique values
0 missing | |
| V7 | numeric | 46 unique values
0 missing | |
| V8 | numeric | 13 unique values
0 missing | |
| V9 | numeric | 34 unique values
0 missing | |
| V10 | numeric | 44 unique values
0 missing | |
| V11 | numeric | 58 unique values
0 missing | |
| V12 | numeric | 30 unique values
0 missing | |
| V13 | numeric | 93 unique values
0 missing | |
| V14 | numeric | 39 unique values
0 missing | |
| V15 | numeric | 35 unique values
0 missing | |
| V16 | numeric | 12 unique values
0 missing | |
| V17 | numeric | 31 unique values
0 missing | |
| V18 | numeric | 60 unique values
0 missing | |
| V19 | numeric | 47 unique values
0 missing | |
| V20 | numeric | 30 unique values
0 missing | |
| V21 | numeric | 50 unique values
0 missing | |
| V22 | numeric | 32 unique values
0 missing | |
| V23 | numeric | 23 unique values
0 missing | |
| V24 | numeric | 36 unique values
0 missing | |
| V25 | numeric | 37 unique values
0 missing | |
| V26 | numeric | 37 unique values
0 missing | |
| V27 | numeric | 35 unique values
0 missing | |
| V28 | numeric | 38 unique values
0 missing | |
| V29 | numeric | 63 unique values
0 missing | |
| V30 | numeric | 49 unique values
0 missing | |
| V31 | numeric | 49 unique values
0 missing | |
| V32 | numeric | 54 unique values
0 missing | |
| V33 | numeric | 42 unique values
0 missing | |
| V34 | numeric | 12 unique values
0 missing | |
| V35 | numeric | 37 unique values
0 missing | |
| V36 | numeric | 41 unique values
0 missing | |
| V37 | numeric | 33 unique values
0 missing | |
| V38 | numeric | 59 unique values
0 missing | |
| V39 | numeric | 32 unique values
0 missing | |
| V40 | numeric | 34 unique values
0 missing | |
| V41 | numeric | 66 unique values
0 missing | |
| V42 | numeric | 42 unique values
0 missing | |
| V43 | numeric | 54 unique values
0 missing | |
| V44 | numeric | 32 unique values
0 missing | |
| V45 | numeric | 67 unique values
0 missing | |
| V46 | numeric | 37 unique values
0 missing | |
| V47 | numeric | 51 unique values
0 missing | |
| V48 | numeric | 77 unique values
0 missing | |
| V49 | numeric | 49 unique values
0 missing | |
| V50 | numeric | 43 unique values
0 missing | |
| V51 | numeric | 62 unique values
0 missing | |
| V52 | numeric | 29 unique values
0 missing | |
| V53 | numeric | 45 unique values
0 missing | |
| V54 | numeric | 49 unique values
0 missing | |
| V55 | numeric | 59 unique values
0 missing | |
| V56 | numeric | 25 unique values
0 missing | |
| V57 | numeric | 30 unique values
0 missing | |
| V58 | numeric | 53 unique values
0 missing | |
| V59 | numeric | 93 unique values
0 missing | |
| V60 | numeric | 40 unique values
0 missing | |
| V61 | numeric | 10 unique values
0 missing | |
| V62 | numeric | 35 unique values
0 missing | |
| V63 | numeric | 54 unique values
0 missing | |
| V64 | numeric | 34 unique values
0 missing | |
107 properties
1600
Number of instances (rows) of the dataset.
65
Number of attributes (columns) of the dataset.
100
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.
64
Number of numeric attributes.
1
Number of nominal attributes.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
100
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
100
The maximum number of distinct values among attributes of the nominal type.
0.69
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
12.45
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
8.33
Third quartile of kurtosis among attributes of the numeric type.
0.94
Average class difference between consecutive instances.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.58
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.05
Maximum standard deviation of attributes of the numeric type.
1
Percentage of instances belonging to the least frequent class.
98.46
Percentage of numeric attributes.
0.02
Third quartile of means among attributes of the numeric type.
0.73
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
0.66
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
16
Number of instances belonging to the least frequent class.
1.54
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.59
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
0.41
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
10.55
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2.72
Third quartile of skewness among attributes of the numeric type.
0.73
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
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.58
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Mean of means among attributes of the numeric type.
0.21
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.24
First quartile of kurtosis among attributes of the numeric type.
0.02
Third quartile of standard deviation of attributes of the numeric type.
0.59
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
0.66
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.01
First quartile of means among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.41
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.73
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.58
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
100
Average number of distinct values among the attributes of the nominal type.
1.16
First quartile of skewness among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.59
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.22
Mean skewness among attributes of the numeric type.
0.01
First quartile of standard deviation of attributes of the numeric type.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.41
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
0.28
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
1
Percentage of instances belonging to the most frequent class.
0.02
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
6.64
Entropy of the target attribute values.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
16
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.17
Minimum kurtosis among attributes of the numeric type.
0.02
Second quartile (Median) of means among attributes of the numeric type.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.98
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
182.37
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.76
Second quartile (Median) of skewness among attributes of the numeric type.
419 tasks
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0 runs - target_feature: Class
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