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📄 breast-cancer.arff

📁 是UCI数据库中的一些有代表性的数据集
💻 ARFF
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% 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%% Num Instances:     286% Num Attributes:    10% Num Continuous:    0 (Int 0 / Real 0)% Num Discrete:      10% Missing values:    9 /  0.3%%%     name                      type enum ints real     missing    distinct  (1)%   1 'age'                     Enum 100%   0%   0%     0 /  0%     6 /  2%   0% %   2 'menopause'               Enum 100%   0%   0%     0 /  0%     3 /  1%   0% %   3 'tumor-size'              Enum 100%   0%   0%     0 /  0%    11 /  4%   0% %   4 'inv-nodes'               Enum 100%   0%   0%     0 /  0%     7 /  2%   0% %   5 'node-caps'               Enum  97%   0%   0%     8 /  3%     2 /  1%   0% %   6 'deg-malig'               Enum 100%   0%   0%     0 /  0%     3 /  1%   0% %   7 'breast'                  Enum 100%   0%   0%     0 /  0%     2 /  1%   0% %   8 'breast-quad'             Enum 100%   0%   0%     1 /  0%     5 /  2%   0% %   9 'irradiat'                Enum 100%   0%   0%     0 /  0%     2 /  1%   0% %  10 'Class'                   Enum 100%   0%   0%     0 /  0%     2 /  1%   0% %%@relation breast-cancer@attribute age {'10-19','20-29','30-39','40-49','50-59','60-69','70-79','80-89','90-99'}@attribute menopause {'lt40','ge40','premeno'}@attribute 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'}@attribute 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'}@attribute node-caps {'yes','no'}@attribute deg-malig {'1','2','3'}@attribute breast {'left','right'}@attribute breast-quad {'left_up','left_low','right_up','right_low','central'}@attribute 'irradiat' {'yes','no'}@attribute 'Class' 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