Data
leaf

leaf

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael G. Mantovani
0 likes downloaded by 8 people , 15 total downloads 0 issues 0 downvotes
  • mf_less_than_80 study_123 study_7 study_88
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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

18 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.
0.05
Number of attributes divided by the number of instances.
6.25
Percentage of nominal attributes.
4.71
Percentage of instances belonging to the most frequent class.
16
Number of instances belonging to the most frequent class.
8
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.
0.05
The predictive accuracy obtained by always predicting the majority class.
93.75
Percentage of numeric attributes.

4 tasks

95 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
48 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Define a new task