%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% Tumor-size treated as the class attribute.
%
% As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
% using instance-based learning with encoding length selection. In Progress
% in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% Citation Request:
%    This breast cancer domain was obtained from the University Medical Centre,
%    Institute of Oncology, Ljubljana, Yugoslavia.  Thanks go to M. Zwitter and 
%    M. Soklic for providing the data.  Please include this citation if you plan
%    to use this database.
% 
% 1. Title: Breast cancer data (Michalski has used this)
% 
% 2. Sources: 
%    -- Matjaz Zwitter & Milan Soklic (physicians)
%       Institute of Oncology 
%       University Medical Center
%       Ljubljana, Yugoslavia
%    -- Donors: Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
%    -- Date: 11 July 1988
% 
% 3. Past Usage: (Several: here are some)
%      -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The 
%         Multi-Purpose Incremental Learning System AQ15 and its Testing 
%         Application to Three Medical Domains.  In Proceedings of the 
%         Fifth National Conference on Artificial Intelligence, 1041-1045,
%         Philadelphia, PA: Morgan Kaufmann.
%         -- accuracy range: 66%-72%
%      -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains.  In 
%         Progress in Machine Learning (from the Proceedings of the 2nd
%         European Working Session on Learning), 11-30, Bled, 
%         Yugoslavia: Sigma Press.
%         -- 8 test results given: 65%-72% accuracy range
%      -- Tan, M., & Eshelman, L. (1988). Using weighted networks to 
%         represent classification knowledge in noisy domains.  Proceedings 
%         of the Fifth International Conference on Machine Learning, 121-134,
%         Ann Arbor, MI.
%         -- 4 systems tested: accuracy range was 68%-73.5%
%     -- Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A
%        Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko
%        & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
%        -- Assistant-86: 78% accuracy
% 
% 4. Relevant Information:
%      This is one of three domains provided by the Oncology Institute
%      that has repeatedly appeared in the machine learning literature.
%      (See also lymphography and primary-tumor.)
% 
%      This data set includes 201 instances of one class and 85 instances of
%      another class.  The instances are described by 9 attributes, some of
%      which are linear and some are nominal.
% 
% 5. Number of Instances: 286
% 
% 6. Number of Attributes: 9 + the class attribute
% 
% 7. Attribute Information:
%    1. Class: no-recurrence-events, recurrence-events
%    2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
%    3. menopause: lt40, ge40, premeno.
%    4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
%                   45-49, 50-54, 55-59.
%    5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26,
%                  27-29, 30-32, 33-35, 36-39.
%    6. node-caps: yes, no.
%    7. deg-malig: 1, 2, 3.
%    8. breast: left, right.
%    9. breast-quad: left-up, left-low, right-up, right-low, central.
%   10. irradiat: yes, no.
% 
% 8. Missing Attribute Values: (denoted by "?")
%    Attribute #:  Number of instances with missing values:
%    6.             8
%    9.             1.
% 
% 9. Class Distribution:
%     1. no-recurrence-events: 201 instances
%     2. recurrence-events: 85 instances
% 

@relation 'breastTumor'
@attribute age real
@attribute menopause { premenopausal, >=40, <40}
@attribute inv-nodes { 0, 2, 3, 1, 7, 10, 16, 5, 8, 6, 4, 25, 9, 17, 15, 13, 14, 11}
@attribute node-caps { no, yes}
@attribute deg-malig { 1, 3, 2}
@attribute breast { right, left}
@attribute breast-quad { left-lower, right-lower, left-upper, right-upper, central}
@attribute irradiation { no, yes}
@attribute recurrence { n, r}
@attribute class real