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
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V6numeric735 unique values
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V8numeric729 unique values
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V12numeric756 unique values
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V14numeric769 unique values
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V15numeric767 unique values
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V16numeric771 unique values
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V17numeric770 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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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
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V29numeric770 unique values
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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
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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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V48numeric783 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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V55numeric723 unique values
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V56numeric731 unique values
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V57numeric724 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
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19 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
Percentage of instances having missing values.
0.94
Average class difference between consecutive instances.
0
Percentage of missing values.
0.04
Number of attributes divided by the number of instances.
98.46
Percentage of numeric attributes.
1
Percentage of instances belonging to the most frequent class.
1.54
Percentage of nominal attributes.
16
Number of instances belonging to the most frequent class.
1
Percentage of instances belonging to the least frequent class.
16
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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