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data/tanveer/breast-cancer/breast-cancer.names

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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