118 lines
4.9 KiB
ReStructuredText
118 lines
4.9 KiB
ReStructuredText
.. _breast_cancer_dataset:
|
|
|
|
Breast cancer wisconsin (diagnostic) dataset
|
|
--------------------------------------------
|
|
|
|
**Data Set Characteristics:**
|
|
|
|
:Number of Instances: 569
|
|
|
|
:Number of Attributes: 30 numeric, predictive attributes and the class
|
|
|
|
:Attribute Information:
|
|
- radius (mean of distances from center to points on the perimeter)
|
|
- texture (standard deviation of gray-scale values)
|
|
- perimeter
|
|
- area
|
|
- smoothness (local variation in radius lengths)
|
|
- compactness (perimeter^2 / area - 1.0)
|
|
- concavity (severity of concave portions of the contour)
|
|
- concave points (number of concave portions of the contour)
|
|
- symmetry
|
|
- fractal dimension ("coastline approximation" - 1)
|
|
|
|
The mean, standard error, and "worst" or largest (mean of the three
|
|
worst/largest values) of these features were computed for each image,
|
|
resulting in 30 features. For instance, field 0 is Mean Radius, field
|
|
10 is Radius SE, field 20 is Worst Radius.
|
|
|
|
- class:
|
|
- WDBC-Malignant
|
|
- WDBC-Benign
|
|
|
|
:Summary Statistics:
|
|
|
|
===================================== ====== ======
|
|
Min Max
|
|
===================================== ====== ======
|
|
radius (mean): 6.981 28.11
|
|
texture (mean): 9.71 39.28
|
|
perimeter (mean): 43.79 188.5
|
|
area (mean): 143.5 2501.0
|
|
smoothness (mean): 0.053 0.163
|
|
compactness (mean): 0.019 0.345
|
|
concavity (mean): 0.0 0.427
|
|
concave points (mean): 0.0 0.201
|
|
symmetry (mean): 0.106 0.304
|
|
fractal dimension (mean): 0.05 0.097
|
|
radius (standard error): 0.112 2.873
|
|
texture (standard error): 0.36 4.885
|
|
perimeter (standard error): 0.757 21.98
|
|
area (standard error): 6.802 542.2
|
|
smoothness (standard error): 0.002 0.031
|
|
compactness (standard error): 0.002 0.135
|
|
concavity (standard error): 0.0 0.396
|
|
concave points (standard error): 0.0 0.053
|
|
symmetry (standard error): 0.008 0.079
|
|
fractal dimension (standard error): 0.001 0.03
|
|
radius (worst): 7.93 36.04
|
|
texture (worst): 12.02 49.54
|
|
perimeter (worst): 50.41 251.2
|
|
area (worst): 185.2 4254.0
|
|
smoothness (worst): 0.071 0.223
|
|
compactness (worst): 0.027 1.058
|
|
concavity (worst): 0.0 1.252
|
|
concave points (worst): 0.0 0.291
|
|
symmetry (worst): 0.156 0.664
|
|
fractal dimension (worst): 0.055 0.208
|
|
===================================== ====== ======
|
|
|
|
:Missing Attribute Values: None
|
|
|
|
:Class Distribution: 212 - Malignant, 357 - Benign
|
|
|
|
:Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
|
|
|
|
:Donor: Nick Street
|
|
|
|
:Date: November, 1995
|
|
|
|
This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
|
|
https://goo.gl/U2Uwz2
|
|
|
|
Features are computed from a digitized image of a fine needle
|
|
aspirate (FNA) of a breast mass. They describe
|
|
characteristics of the cell nuclei present in the image.
|
|
|
|
Separating plane described above was obtained using
|
|
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
|
|
Construction Via Linear Programming." Proceedings of the 4th
|
|
Midwest Artificial Intelligence and Cognitive Science Society,
|
|
pp. 97-101, 1992], a classification method which uses linear
|
|
programming to construct a decision tree. Relevant features
|
|
were selected using an exhaustive search in the space of 1-4
|
|
features and 1-3 separating planes.
|
|
|
|
The actual linear program used to obtain the separating plane
|
|
in the 3-dimensional space is that described in:
|
|
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
|
|
Programming Discrimination of Two Linearly Inseparable Sets",
|
|
Optimization Methods and Software 1, 1992, 23-34].
|
|
|
|
This database is also available through the UW CS ftp server:
|
|
|
|
ftp ftp.cs.wisc.edu
|
|
cd math-prog/cpo-dataset/machine-learn/WDBC/
|
|
|
|
.. topic:: References
|
|
|
|
- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
|
|
for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
|
|
Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
|
|
San Jose, CA, 1993.
|
|
- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
|
|
prognosis via linear programming. Operations Research, 43(4), pages 570-577,
|
|
July-August 1995.
|
|
- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
|
|
to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
|
|
163-171. |