% 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 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