Data
one-hundred-plants-margin

one-hundred-plants-margin

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
1 likes downloaded by 13 people , 25 total downloads 0 issues 0 downvotes
  • OpenML100 study_123 study_14 study_34 study_50 study_7
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) - 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
0 missing
V7numeric46 unique values
0 missing
V8numeric13 unique values
0 missing
V9numeric34 unique values
0 missing
V10numeric44 unique values
0 missing
V11numeric58 unique values
0 missing
V12numeric30 unique values
0 missing
V13numeric93 unique values
0 missing
V14numeric39 unique values
0 missing
V15numeric35 unique values
0 missing
V16numeric12 unique values
0 missing
V17numeric31 unique values
0 missing
V18numeric60 unique values
0 missing
V19numeric47 unique values
0 missing
V20numeric30 unique values
0 missing
V21numeric50 unique values
0 missing
V22numeric32 unique values
0 missing
V23numeric23 unique values
0 missing
V24numeric36 unique values
0 missing
V25numeric37 unique values
0 missing
V26numeric37 unique values
0 missing
V27numeric35 unique values
0 missing
V28numeric38 unique values
0 missing
V29numeric63 unique values
0 missing
V30numeric49 unique values
0 missing
V31numeric49 unique values
0 missing
V32numeric54 unique values
0 missing
V33numeric42 unique values
0 missing
V34numeric12 unique values
0 missing
V35numeric37 unique values
0 missing
V36numeric41 unique values
0 missing
V37numeric33 unique values
0 missing
V38numeric59 unique values
0 missing
V39numeric32 unique values
0 missing
V40numeric34 unique values
0 missing
V41numeric66 unique values
0 missing
V42numeric42 unique values
0 missing
V43numeric54 unique values
0 missing
V44numeric32 unique values
0 missing
V45numeric67 unique values
0 missing
V46numeric37 unique values
0 missing
V47numeric51 unique values
0 missing
V48numeric77 unique values
0 missing
V49numeric49 unique values
0 missing
V50numeric43 unique values
0 missing
V51numeric62 unique values
0 missing
V52numeric29 unique values
0 missing
V53numeric45 unique values
0 missing
V54numeric49 unique values
0 missing
V55numeric59 unique values
0 missing
V56numeric25 unique values
0 missing
V57numeric30 unique values
0 missing
V58numeric53 unique values
0 missing
V59numeric93 unique values
0 missing
V60numeric40 unique values
0 missing
V61numeric10 unique values
0 missing
V62numeric35 unique values
0 missing
V63numeric54 unique values
0 missing
V64numeric34 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.
0.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.17
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
182.37
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
8.33
Third quartile of kurtosis among attributes of the numeric type.
0.04
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.
0.02
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.
0.69
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
2.72
Third quartile of skewness among attributes of the numeric type.
12.45
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
1.24
First quartile of kurtosis among attributes of the numeric type.
0.02
Third quartile of standard deviation of attributes of the numeric type.
0.05
Maximum standard deviation of attributes of the numeric type.
1
Percentage of instances belonging to the least frequent class.
0.01
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.
10.55
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
1.16
First quartile of skewness among attributes of the numeric type.
0.02
Mean of means among attributes of the numeric type.
0.01
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.
3.27
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.
0.02
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.
2.22
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.
0.02
Mean standard deviation of attributes of the numeric type.
1.76
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.

409 tasks

9620 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
41 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: leaf
1308 runs - target_feature: Class
1308 runs - target_feature: Class
1307 runs - target_feature: Class
1306 runs - target_feature: Class
1305 runs - target_feature: Class
1305 runs - target_feature: Class
1304 runs - target_feature: Class
1304 runs - target_feature: Class
1304 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1303 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1296 runs - target_feature: Class
1296 runs - target_feature: Class
1296 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
Define a new task