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one-hundred-plants-texture

one-hundred-plants-texture

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael Gomes Mantovani
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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) - 2012 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. One-hundred plant species leaves data set (class = texture) The data directory contains the binary images (masks) of the leaf samples. The colour images are not included. 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). 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 Sources (a) Original owners of colour Leaves Samples: James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. (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 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.

65 features

Class (target)nominal100 unique values
0 missing
V48numeric131 unique values
0 missing
V34numeric170 unique values
0 missing
V47numeric77 unique values
0 missing
V46numeric121 unique values
0 missing
V45numeric101 unique values
0 missing
V44numeric176 unique values
0 missing
V43numeric88 unique values
0 missing
V42numeric61 unique values
0 missing
V41numeric152 unique values
0 missing
V40numeric118 unique values
0 missing
V39numeric100 unique values
0 missing
V38numeric121 unique values
0 missing
V37numeric173 unique values
0 missing
V36numeric73 unique values
0 missing
V35numeric65 unique values
0 missing
V33numeric184 unique values
0 missing
V49numeric73 unique values
0 missing
V50numeric129 unique values
0 missing
V51numeric132 unique values
0 missing
V52numeric73 unique values
0 missing
V53numeric98 unique values
0 missing
V54numeric119 unique values
0 missing
V55numeric224 unique values
0 missing
V56numeric91 unique values
0 missing
V57numeric109 unique values
0 missing
V58numeric116 unique values
0 missing
V59numeric85 unique values
0 missing
V60numeric125 unique values
0 missing
V61numeric64 unique values
0 missing
V62numeric141 unique values
0 missing
V63numeric70 unique values
0 missing
V64numeric108 unique values
0 missing
V17numeric116 unique values
0 missing
V2numeric91 unique values
0 missing
V3numeric72 unique values
0 missing
V4numeric101 unique values
0 missing
V5numeric154 unique values
0 missing
V6numeric97 unique values
0 missing
V7numeric101 unique values
0 missing
V8numeric116 unique values
0 missing
V9numeric110 unique values
0 missing
V10numeric146 unique values
0 missing
V11numeric140 unique values
0 missing
V12numeric195 unique values
0 missing
V13numeric76 unique values
0 missing
V14numeric91 unique values
0 missing
V15numeric104 unique values
0 missing
V16numeric63 unique values
0 missing
V1numeric151 unique values
0 missing
V18numeric73 unique values
0 missing
V19numeric134 unique values
0 missing
V20numeric92 unique values
0 missing
V21numeric64 unique values
0 missing
V22numeric101 unique values
0 missing
V23numeric114 unique values
0 missing
V24numeric95 unique values
0 missing
V25numeric82 unique values
0 missing
V26numeric164 unique values
0 missing
V27numeric138 unique values
0 missing
V28numeric94 unique values
0 missing
V29numeric119 unique values
0 missing
V30numeric67 unique values
0 missing
V31numeric124 unique values
0 missing
V32numeric84 unique values
0 missing

107 properties

1599
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.
0.94
Average class difference between consecutive instances.
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
0.58
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
0.73
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
0.58
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
0.41
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
0.73
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
0.58
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
0.41
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
6.64
Entropy of the target attribute values.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.98
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.04
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.
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.54
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1
Percentage of instances belonging to the most frequent class.
16
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
119.53
Maximum kurtosis among attributes of the numeric type.
0.04
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
100
The maximum number of distinct values among attributes of the nominal type.
8.9
Maximum skewness among attributes of the numeric type.
0.07
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
15.69
Mean kurtosis among attributes of the numeric type.
0.02
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.
100
Average number of distinct values among the attributes of the nominal type.
3.14
Mean skewness among attributes of the numeric type.
0.03
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
0.98
Minimum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
The minimal number of distinct values among attributes of the nominal type.
0.96
Minimum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
0.94
Percentage of instances belonging to the least frequent class.
15
Number of instances belonging to the least frequent class.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
98.46
Percentage of numeric attributes.
1.54
Percentage of nominal attributes.
First quartile of entropy among attributes.
6.34
First quartile of kurtosis among attributes of the numeric type.
0.01
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
2.25
First quartile of skewness among attributes of the numeric type.
0.02
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
11.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.02
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.
2.86
Second quartile (Median) of skewness among attributes of the numeric type.
0.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
16.02
Third quartile of kurtosis among attributes of the numeric type.
0.02
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
3.55
Third quartile of skewness among attributes of the numeric type.
0.03
Third quartile of standard deviation of attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.62
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.62
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.62
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.37
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.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.24
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 5 times 2-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: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10% 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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