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breast-cancer.names
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| Citation Request:
| This breast cancer databases was obtained from the University of Wisconsin
| Hospitals, Madison from Dr. William H. Wolberg. If you publish results
| when using this database, then please include this information in your
| acknowledgements. Also, please cite one or more of:
|
| 1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear
| programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.
|
| 2. William H. Wolberg and O.L. Mangasarian: "Multisurface method of
| pattern separation for medical diagnosis applied to breast cytology",
| Proceedings of the National Academy of Sciences, U.S.A., Volume 87,
| December 1990, pp 9193-9196.
|
| 3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition
| via linear programming: Theory and application to medical diagnosis",
| in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying
| Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30.
|
| 4. K. P. Bennett & O. L. Mangasarian: "Robust linear programming
| discrimination of two linearly inseparable sets", Optimization Methods
| and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).
|
| 1. Title: Wisconsin Breast Cancer Database (January 8, 1991)
|
| 2. Sources:
| -- Dr. WIlliam H. Wolberg (physician)
| University of Wisconsin Hospitals
| Madison, Wisconsin
| USA
| -- Donor: Olvi Mangasarian ([email protected])
| Received by David W. Aha ([email protected])
| -- Date: 15 July 1992
|
| 3. Past Usage:
|
| Attributes 2 through 10 have been used to represent instances.
| Each instance has one of 2 possible classes: benign or malignant.
|
| 1. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of
| pattern separation for medical diagnosis applied to breast cytology. In
| {\it Proceedings of the National Academy of Sciences}, {\it 87},
| 9193--9196.
| -- Size of data set: only 369 instances (at that point in time)
| -- Collected classification results: 1 trial only
| -- Two pairs of parallel hyperplanes were found to be consistent with
| 50% of the data
| -- Accuracy on remaining 50% of dataset: 93.5%
| -- Three pairs of parallel hyperplanes were found to be consistent with
| 67% of data
| -- Accuracy on remaining 33% of dataset: 95.9%
|
| 2. Zhang,~J. (1992). Selecting typical instances in instance-based
| learning. In {\it Proceedings of the Ninth International Machine
| Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan
| Kaufmann.
| -- Size of data set: only 369 instances (at that point in time)
| -- Applied 4 instance-based learning algorithms
| -- Collected classification results averaged over 10 trials
| -- Best accuracy result:
| -- 1-nearest neighbor: 93.7%
| -- trained on 200 instances, tested on the other 169
| -- Also of interest:
| -- Using only typical instances: 92.2% (storing only 23.1 instances)
| -- trained on 200 instances, tested on the other 169
|
| 4. Relevant Information:
|
| Samples arrive periodically as Dr. Wolberg reports his clinical cases.
| The database therefore reflects this chronological grouping of the data.
| This grouping information appears immediately below, having been removed
| from the data itself:
|
| Group 1: 367 instances (January 1989)
| Group 2: 70 instances (October 1989)
| Group 3: 31 instances (February 1990)
| Group 4: 17 instances (April 1990)
| Group 5: 48 instances (August 1990)
| Group 6: 49 instances (Updated January 1991)
| Group 7: 31 instances (June 1991)
| Group 8: 86 instances (November 1991)
| -----------------------------------------
| Total: 699 points (as of the donated datbase on 15 July 1992)
|
| Note that the results summarized above in Past Usage refer to a dataset
| of size 369, while Group 1 has only 367 instances. This is because it
| originally contained 369 instances; 2 were removed. The following
| statements summarizes changes to the original Group 1's set of data:
|
| ##### Group 1 : 367 points: 200B 167M (January 1989)
| ##### Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805
| ##### Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record
| ##### : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial
| ##### : Changed 0 to 1 in field 6 of sample 1219406
| ##### : Changed 0 to 1 in field 8 of following sample:
| ##### : 1182404,2,3,1,1,1,2,0,1,1,1
|
| 5. Number of Instances: 699 (as of 15 July 1992)
|
| 6. Number of Attributes: 10 plus the class attribute
|
| 7. Attribute Information: (class attribute has been moved to last column)
|
| # Attribute Domain
| -- -----------------------------------------
| 1. Sample code number id number
| 2. Clump Thickness 1 - 10
| 3. Uniformity of Cell Size 1 - 10
| 4. Uniformity of Cell Shape 1 - 10
| 5. Marginal Adhesion 1 - 10
| 6. Single Epithelial Cell Size 1 - 10
| 7. Bare Nuclei 1 - 10
| 8. Bland Chromatin 1 - 10
| 9. Normal Nucleoli 1 - 10
| 10. Mitoses 1 - 10
| 11. Class: (2 for benign, 4 for malignant)
|
| 8. Missing attribute values: 16
|
| There are 16 instances in Groups 1 to 6 that contain a single missing
| (i.e., unavailable) attribute value, now denoted by "?".
|
| 9. Class distribution:
|
| Benign: 458 (65.5%)
| Malignant: 241 (34.5%)
2, 4
Sample code number: ignore
Clump Thickness: continuous
Uniformity of Cell Size: continuous
Uniformity of Cell Shape: continuous
Marginal Adhesion: continuous
Single Epithelial Cell Size: continuous
Bare Nuclei: continuous
Bland Chromatin: continuous
Normal Nucleoli: continuous
Mitoses: continuous