% Retrieved from http://repository.seasr.org/Datasets/UCI/arff/soybean.arff
% 
% # Notes: The large soybean database (soybean-large-data.arff) and it's 
%        corresponding test database (soybean-large-test.arff) combined
%        into a single file.
%
% 1. Title: Large Soybean Database
% 
% 2. Sources:
%      (a) R.S. Michalski and R.L. Chilausky "Learning by Being Told and
%          Learning from Examples: An Experimental Comparison of the Two
%    Methods of Knowledge Acquisition in the Context of Developing
%    an Expert System for Soybean Disease Diagnosis", International
%    Journal of Policy Analysis and Information Systems, Vol. 4,
%    No. 2, 1980.
%      (b) Donor: Ming Tan & Jeff Schlimmer (Jeff.Schlimmer%cs.cmu.edu)
%      (c) Date: 11 July 1988
% 
% 3. Past Usage:
%     1. See above.
%     2. Tan, M., & Eshelman, L. (1988). Using weighted networks to represent
%        classification knowledge in noisy domains.  Proceedings of the Fifth
%        International Conference on Machine Learning (pp. 121-134). Ann Arbor,
%         Michigan: Morgan Kaufmann.
%         -- IWN recorded a 97.1% classification accuracy 
%            -- 290 training and 340 test instances
%      3. Fisher,D.H. & Schlimmer,J.C. (1988). Concept Simplification and
%         Predictive Accuracy. Proceedings of the Fifth
%         International Conference on Machine Learning (pp. 22-28). Ann Arbor,
%         Michigan: Morgan Kaufmann.
%         -- Notes why this database is highly predictable
% 
% 4. Relevant Information Paragraph:
%     There are 19 classes, only the first 15 of which have been used in prior
%     work.  The folklore seems to be that the last four classes are
%     unjustified by the data since they have so few examples.
%     There are 35 categorical attributes, some nominal and some ordered.  The
%     value ``dna'' means does not apply.  The values for attributes are
%     encoded numerically, with the first value encoded as ``0,'' the second as
%     ``1,'' and so forth.  An unknown values is encoded as ``?''.
% 
% 5. Number of Instances: 683 (one moved to test set)
% 
% 6. Number of Attributes: 35 (all have been nominalized)
% 
% 7. Attribute Information: 
%    -- 19 Classes
%     diaporthe-stem-canker, charcoal-rot, rhizoctonia-root-rot,
%     phytophthora-rot, brown-stem-rot, powdery-mildew,
%     downy-mildew, brown-spot, bacterial-blight,
%     bacterial-pustule, purple-seed-stain, anthracnose,
%     phyllosticta-leaf-spot, alternarialeaf-spot,
%     frog-eye-leaf-spot, diaporthe-pod-&-stem-blight,
%     cyst-nematode, 2-4-d-injury, herbicide-injury.    
%
%    1. date:       april,may,june,july,august,september,october,?.
%    2. plant-stand:    normal,lt-normal,?.
%    3. precip:     lt-norm,norm,gt-norm,?.
%    4. temp:       lt-norm,norm,gt-norm,?.
%    5. hail:       yes,no,?.
%    6. crop-hist:  diff-lst-year,same-lst-yr,same-lst-two-yrs,
%                        same-lst-sev-yrs,?.
%    7. area-damaged:   scattered,low-areas,upper-areas,whole-field,?.
%    8. severity:   minor,pot-severe,severe,?.
%    9. seed-tmt:   none,fungicide,other,?.
%   10. germination:    '90-100%','80-89%','lt-80%',?.
%   11. plant-growth:   norm,abnorm,?.
%   12. leaves:     norm,abnorm.
%   13. leafspots-halo: absent,yellow-halos,no-yellow-halos,?.
%   14. leafspots-marg: w-s-marg,no-w-s-marg,dna,?.
%   15. leafspot-size:  lt-1/8,gt-1/8,dna,?.
%   16. leaf-shread:    absent,present,?.
%   17. leaf-malf:  absent,present,?.
%   18. leaf-mild:  absent,upper-surf,lower-surf,?.
%   19. stem:       norm,abnorm,?.
%   20. lodging:        yes,no,?.
%   21. stem-cankers:   absent,below-soil,above-soil,above-sec-nde,?.
%   22. canker-lesion:  dna,brown,dk-brown-blk,tan,?.
%   23. fruiting-bodies:    absent,present,?.
%   24. external decay: absent,firm-and-dry,watery,?.
%   25. mycelium:   absent,present,?.
%   26. int-discolor:   none,brown,black,?.
%   27. sclerotia:  absent,present,?.
%   28. fruit-pods: norm,diseased,few-present,dna,?.
%   29. fruit spots:    absent,colored,brown-w/blk-specks,distort,dna,?.
%   30. seed:       norm,abnorm,?.
%   31. mold-growth:    absent,present,?.
%   32. seed-discolor:  absent,present,?.
%   33. seed-size:  norm,lt-norm,?.
%   34. shriveling: absent,present,?.
%   35. roots:      norm,rotted,galls-cysts,?.
