Data
one-hundred-plants-shape

one-hundred-plants-shape

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • OpenML100 study_123 study_14 study_34 study_50 study_52 study_7
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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) - 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 = Shape). ### 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 [dataset provided here] * 'data_Tex_64.txt' -> prediction based on texture * 'data_Mar_64.txt' -> prediction based on margin 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)nominal100 unique values
0 missing
V1numeric788 unique values
0 missing
V2numeric801 unique values
0 missing
V3numeric774 unique values
0 missing
V4numeric777 unique values
0 missing
V5numeric754 unique values
0 missing
V6numeric735 unique values
0 missing
V7numeric719 unique values
0 missing
V8numeric729 unique values
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V9numeric715 unique values
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V10numeric739 unique values
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V11numeric729 unique values
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V12numeric756 unique values
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V13numeric738 unique values
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V14numeric769 unique values
0 missing
V15numeric767 unique values
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V16numeric771 unique values
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V17numeric770 unique values
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V18numeric769 unique values
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V19numeric761 unique values
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V20numeric758 unique values
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V21numeric752 unique values
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V22numeric751 unique values
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V23numeric731 unique values
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V24numeric742 unique values
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V25numeric730 unique values
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V26numeric733 unique values
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V27numeric736 unique values
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V28numeric762 unique values
0 missing
V29numeric770 unique values
0 missing
V30numeric783 unique values
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V31numeric797 unique values
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V32numeric813 unique values
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V33numeric824 unique values
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V34numeric801 unique values
0 missing
V35numeric791 unique values
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V36numeric766 unique values
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V37numeric762 unique values
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V38numeric739 unique values
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V39numeric718 unique values
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V40numeric697 unique values
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V41numeric720 unique values
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V42numeric730 unique values
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V43numeric741 unique values
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V44numeric743 unique values
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V45numeric758 unique values
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V46numeric763 unique values
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V47numeric761 unique values
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V48numeric783 unique values
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V49numeric769 unique values
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V50numeric786 unique values
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V51numeric766 unique values
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V52numeric749 unique values
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V53numeric739 unique values
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V54numeric737 unique values
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V55numeric723 unique values
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V56numeric731 unique values
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V57numeric724 unique values
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V58numeric748 unique values
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V59numeric749 unique values
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V60numeric766 unique values
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V61numeric754 unique values
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V62numeric766 unique values
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V63numeric785 unique values
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V64numeric804 unique values
0 missing

62 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
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.12
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
8.41
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
5.53
Third quartile of kurtosis among attributes of the numeric type.
0
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
98.46
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
100
The minimal number of distinct values among attributes of the nominal type.
1.54
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
100
The maximum number of distinct values among attributes of the nominal type.
0.29
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.01
Third quartile of skewness among attributes of the numeric type.
2.31
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
1.17
First quartile of kurtosis among attributes of the numeric type.
0
Third quartile of standard deviation of attributes of the numeric type.
0
Maximum standard deviation of attributes of the numeric type.
1
Percentage of instances belonging to the least frequent class.
0
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.
16
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
3.8
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
0.62
First quartile of skewness among attributes of the numeric type.
0
Mean of means among attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.94
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
6.64
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.
4.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
100
Average number of distinct values among the attributes of the nominal type.
0
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.
1.39
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1
Percentage of instances belonging to the most frequent class.
0
Mean standard deviation of attributes of the numeric type.
1.71
Second quartile (Median) of skewness among attributes of the numeric type.
16
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

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