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