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data info file

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2<head>
3<title>Breast Cancer Wisconsin Data Base</title>
4</head>
5<body>
6<h1>Info on Breast Cancer Wisconsin Data Base</h1>
7<pre>
8Citation Request:
9   This breast cancer domain was obtained from the University Medical Centre,
10   Institute of Oncology, Ljubljana, Yugoslavia.  Thanks go to M. Zwitter and
11   M. Soklic for providing the data.  Please include this citation if you plan
12   to use this database.
13
141. Title: Breast cancer data (Michalski has used this)
15
162. Sources:
17   -- Matjaz Zwitter & Milan Soklic (physicians)
18      Institute of Oncology
19      University Medical Center
20      Ljubljana, Yugoslavia
21   -- Donors: Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
22   -- Date: 11 July 1988
23
243. Past Usage: (Several: here are some)
25     -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The
26        Multi-Purpose Incremental Learning System AQ15 and its Testing
27        Application to Three Medical Domains.  In Proceedings of the
28        Fifth National Conference on Artificial Intelligence, 1041-1045,
29        Philadelphia, PA: Morgan Kaufmann.
30        -- accuracy range: 66%-72%
31     -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains.  In
32        Progress in Machine Learning (from the Proceedings of the 2nd
33        European Working Session on Learning), 11-30, Bled,
34        Yugoslavia: Sigma Press.
35        -- 8 test results given: 65%-72% accuracy range
36     -- Tan, M., & Eshelman, L. (1988). Using weighted networks to
37        represent classification knowledge in noisy domains.  Proceedings
38        of the Fifth International Conference on Machine Learning, 121-134,
39        Ann Arbor, MI.
40        -- 4 systems tested: accuracy range was 68%-73.5%
41    -- Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A
42       Knowledge-Elicitation Tool for Sophisticated Users.  In I.Bratko
43       & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press.
44       -- Assistant-86: 78% accuracy
45
464. Relevant Information:
47     This is one of three domains provided by the Oncology Institute
48     that has repeatedly appeared in the machine learning literature.
49     (See also lymphography and primary-tumor.)
50
51     This data set includes 201 instances of one class and 85 instances of
52     another class.  The instances are described by 9 attributes, some of
53     which are linear and some are nominal.
54
555. Number of Instances: 286
56
576. Number of Attributes: 9 + the class attribute
58
597. Attribute Information:
60   1. Class: no-recurrence-events, recurrence-events
61   2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99.
62   3. menopause: lt40, ge40, premeno.
63   4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
64                  45-49, 50-54, 55-59.
65   5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26,
66                 27-29, 30-32, 33-35, 36-39.
67   6. node-caps: yes, no.
68   7. deg-malig: 1, 2, 3.
69   8. breast: left, right.
70   9. breast-quad: left-up, left-low, right-up, right-low, central.
71  10. irradiat: yes, no.
72
738. Missing Attribute Values: (denoted by "?")
74   Attribute #:  Number of instances with missing values:
75   6.             8
76   9.             1.
77
789. Class Distribution:
79    1. no-recurrence-events: 201 instances
80    2. recurrence-events: 85 instances
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