Data
leaf

leaf

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael G. Mantovani
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Author: Pedro F.B. Silva, Andre R.S. Marcal, Rubim M. Almeida da Silva Source: UCI Please cite: 'Evaluation of Features for Leaf Discrimination', Pedro F.B. Silva, Andre R.S. Marcal, Rubim M. Almeida da Silva (2013). Springer Lecture Notes in Computer Science, Vol. 7950, 197-204. Abstract: This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species. Source: This dataset was created by Pedro F. B. Silva and Andre R. S. Marcal using leaf specimens collected by Rubim Almeida da Silva at the Faculty of Science, University of Porto, Portugal. Data Set Information: For further details on this dataset and/or its attributes, please read the 'ReadMe.pdf' file included and/or consult the Master's Thesis 'Development of a System for Automatic Plant Species Recognition' available at [Web Link]. Attribute Information: 1. Class (Species) 2. Specimen Number 3. Eccentricity 4. Aspect Ratio 5. Elongation 6. Solidity 7. Stochastic Convexity 8. Isoperimetric Factor 9. Maximal Indentation Depth 10. Lobedness 11. Average Intensity 12. Average Contrast 13. Smoothness 14. Third moment 15. Uniformity 16. Entropy

16 features

Class (target)nominal30 unique values
0 missing
V1numeric16 unique values
0 missing
V2numeric339 unique values
0 missing
V3numeric334 unique values
0 missing
V4numeric339 unique values
0 missing
V5numeric333 unique values
0 missing
V6numeric88 unique values
0 missing
V7numeric339 unique values
0 missing
V8numeric340 unique values
0 missing
V9numeric339 unique values
0 missing
V10numeric338 unique values
0 missing
V11numeric339 unique values
0 missing
V12numeric338 unique values
0 missing
V13numeric336 unique values
0 missing
V14numeric263 unique values
0 missing
V15numeric337 unique values
0 missing

62 properties

340
Number of instances (rows) of the dataset.
16
Number of attributes (columns) of the dataset.
30
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.
15
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-0.9
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
11.97
Maximum kurtosis among attributes of the numeric type.
0
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
5.78
Third quartile of kurtosis among attributes of the numeric type.
6.28
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
93.75
Percentage of numeric attributes.
0.94
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
30
The minimal number of distinct values among attributes of the nominal type.
6.25
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
30
The maximum number of distinct values among attributes of the nominal type.
-2.63
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.78
Third quartile of skewness among attributes of the numeric type.
3.33
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
-0.53
First quartile of kurtosis among attributes of the numeric type.
0.58
Third quartile of standard deviation of attributes of the numeric type.
3.46
Maximum standard deviation of attributes of the numeric type.
2.35
Percentage of instances belonging to the least frequent class.
0.04
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.
8
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
2.9
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.48
First quartile of skewness among attributes of the numeric type.
0.95
Mean of means among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
0.91
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
4.89
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.
1.39
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.05
Number of attributes divided by the number of instances.
30
Average number of distinct values among the attributes of the nominal type.
0.52
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.
0.66
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
4.71
Percentage of instances belonging to the most frequent class.
0.58
Mean standard deviation of attributes of the numeric type.
0.49
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.

4 tasks

81 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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