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one-hundred-plants-margin

one-hundred-plants-margin

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Author: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. Source: UCI 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. 1. One-hundred plant species leaves data set (class = margin). 2. Sources: (a) Original owners of colour Leaves Samples: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. The colour images are not included in this submission. 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 (c) Date received 03/12/2012 3. Past Usage: (a) This is a new data set, provisional paper: 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. (b) Previous parts of the data set relate to feature extraction of leaves from: J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor cooccurrences. Advances in Visual Computing, pages 669ñ677, 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 345ñ353. Springer, 2010. 4. Relevant Information Paragraph: The data directory contains the binary images (masks) of the leaf samples. The colour images are not included. The data set features are organised as the following: * 'data_Sha_64.txt' * 'data_Tex_64.txt' * [THIS DATASET]'data_Mar_64.txt' One file for each 64-element feature vectors. Each row begins with the class label. The remaining 64 elements is the feature vector. 5. Number of Instances 1600 samples each of three features (16 samples per leaf class). 6. Number of Attributes Three 64 element feature vectors per sample. 7. Vectors There are three features: Shape, Margin and Texture. As discussed in the paper(s) above. For Each feature, a 64 element vector is given per sample of leaf. These vectors are taken as a contigous descriptors (for shape) or histograms (for texture and margin). 8. Missing Attribute Values: none 9. Class Distribution: 16 instances per class

65 features

Class (target)nominal100 unique values
0 missing
V1numeric46 unique values
0 missing
V2numeric90 unique values
0 missing
V3numeric69 unique values
0 missing
V4numeric78 unique values
0 missing
V5numeric53 unique values
0 missing
V6numeric121 unique values
0 missing
V7numeric46 unique values
0 missing
V8numeric13 unique values
0 missing
V9numeric34 unique values
0 missing
V10numeric44 unique values
0 missing
V11numeric58 unique values
0 missing
V12numeric30 unique values
0 missing
V13numeric93 unique values
0 missing
V14numeric39 unique values
0 missing
V15numeric35 unique values
0 missing
V16numeric12 unique values
0 missing
V17numeric31 unique values
0 missing
V18numeric60 unique values
0 missing
V19numeric47 unique values
0 missing
V20numeric30 unique values
0 missing
V21numeric50 unique values
0 missing
V22numeric32 unique values
0 missing
V23numeric23 unique values
0 missing
V24numeric36 unique values
0 missing
V25numeric37 unique values
0 missing
V26numeric37 unique values
0 missing
V27numeric35 unique values
0 missing
V28numeric38 unique values
0 missing
V29numeric63 unique values
0 missing
V30numeric49 unique values
0 missing
V31numeric49 unique values
0 missing
V32numeric54 unique values
0 missing
V33numeric42 unique values
0 missing
V34numeric12 unique values
0 missing
V35numeric37 unique values
0 missing
V36numeric41 unique values
0 missing
V37numeric33 unique values
0 missing
V38numeric59 unique values
0 missing
V39numeric32 unique values
0 missing
V40numeric34 unique values
0 missing
V41numeric66 unique values
0 missing
V42numeric42 unique values
0 missing
V43numeric54 unique values
0 missing
V44numeric32 unique values
0 missing
V45numeric67 unique values
0 missing
V46numeric37 unique values
0 missing
V47numeric51 unique values
0 missing
V48numeric77 unique values
0 missing
V49numeric49 unique values
0 missing
V50numeric43 unique values
0 missing
V51numeric62 unique values
0 missing
V52numeric29 unique values
0 missing
V53numeric45 unique values
0 missing
V54numeric49 unique values
0 missing
V55numeric59 unique values
0 missing
V56numeric25 unique values
0 missing
V57numeric30 unique values
0 missing
V58numeric53 unique values
0 missing
V59numeric93 unique values
0 missing
V60numeric40 unique values
0 missing
V61numeric10 unique values
0 missing
V62numeric35 unique values
0 missing
V63numeric54 unique values
0 missing
V64numeric34 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.
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.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.98
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
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.
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
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.
0.67
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.
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.
0.66
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.
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.
0.33
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
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
0.67
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
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
0.66
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
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.
0.41
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
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.
0.73
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
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
0.59
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
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
0.41
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
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.
0.33
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
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.
0.8
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
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.
0.66
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
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.
0.33
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
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.
0.8
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

11 tasks

17 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 5 times 2-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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