Data
one-hundred-plants-texture

one-hundred-plants-texture

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_14 study_1 study_164 study_668 study_1651 study_1738 study_2102 study_11825 study_13073 study_4714 study_11237 study_11460 study_12080 study_12250 study_13007 study_2907 study_4248 study_4562 study_6936 study_13025 study_1256 study_4890 study_6861 study_11552 study_12080 study_1544 study_4332 study_4630 study_5972 study_12195 study_2018 study_6668 study_6071 study_6824 study_10252 study_10654 study_2479 study_4753 study_10175 study_1396 study_4163 study_6997 study_7020 study_7432 study_10272 study_3367 study_5736 study_6645 study_11684 study_668 study_1651 study_1952 study_2838 study_3344 study_3686 study_7223 study_12392 study_1213 study_4654 study_7021 study_7433 study_7491 study_7635 study_10966
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
V1numeric151 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
V17numeric116 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
V33numeric184 unique values
0 missing
V34numeric170 unique values
0 missing
V35numeric65 unique values
0 missing
V36numeric73 unique values
0 missing
V37numeric173 unique values
0 missing
V38numeric121 unique values
0 missing
V39numeric100 unique values
0 missing
V40numeric118 unique values
0 missing
V41numeric152 unique values
0 missing
V42numeric61 unique values
0 missing
V43numeric88 unique values
0 missing
V44numeric176 unique values
0 missing
V45numeric101 unique values
0 missing
V46numeric121 unique values
0 missing
V47numeric77 unique values
0 missing
V48numeric131 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

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

11 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - 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: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - 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: 10 times 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
Define a new task