Data
one-hundred-plants-margin

one-hundred-plants-margin

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. 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) - 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 = Margin). ### 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 * 'data_Tex_64.txt' -> prediction based on texture * 'data_Mar_64.txt' -> prediction based on margin [dataset provided here] 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
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
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V7numeric46 unique values
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V8numeric13 unique values
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V9numeric34 unique values
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V10numeric44 unique values
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V11numeric58 unique values
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V12numeric30 unique values
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V13numeric93 unique values
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V14numeric39 unique values
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V15numeric35 unique values
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V16numeric12 unique values
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V17numeric31 unique values
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V18numeric60 unique values
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V19numeric47 unique values
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V20numeric30 unique values
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V21numeric50 unique values
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V22numeric32 unique values
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V23numeric23 unique values
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V24numeric36 unique values
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V25numeric37 unique values
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V26numeric37 unique values
0 missing
V27numeric35 unique values
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V28numeric38 unique values
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V29numeric63 unique values
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V30numeric49 unique values
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V31numeric49 unique values
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V32numeric54 unique values
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V33numeric42 unique values
0 missing
V34numeric12 unique values
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V35numeric37 unique values
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V36numeric41 unique values
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V37numeric33 unique values
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V38numeric59 unique values
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V39numeric32 unique values
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V40numeric34 unique values
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V41numeric66 unique values
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V42numeric42 unique values
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V43numeric54 unique values
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V44numeric32 unique values
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V45numeric67 unique values
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V46numeric37 unique values
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V47numeric51 unique values
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V48numeric77 unique values
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V49numeric49 unique values
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V50numeric43 unique values
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V51numeric62 unique values
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V52numeric29 unique values
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V53numeric45 unique values
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V54numeric49 unique values
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V55numeric59 unique values
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V56numeric25 unique values
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V57numeric30 unique values
0 missing
V58numeric53 unique values
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V59numeric93 unique values
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V60numeric40 unique values
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V61numeric10 unique values
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V62numeric35 unique values
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V63numeric54 unique values
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V64numeric34 unique values
0 missing

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.
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.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.94
Average class difference between consecutive instances.
98.46
Percentage of numeric attributes.
0.04
Number of attributes divided by the number of instances.
1.54
Percentage of nominal attributes.
1
Percentage of instances belonging to the most frequent class.

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