Predicting Time To Recur (field 3 in recurrent records).
Each record represents follow-up data for one breast cancer case. These are
consecutive patients seen by Dr. Wolberg since 1984, and include only those
cases exhibiting invasive breast cancer and no evidence of distant metastases
at the time of diagnosis.
The first 30 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. A few of the images can be found at
http://www.cs.wisc.edu/~street/images/
The separation 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].
The Recurrence Surface Approximation (RSA) method is a linear programming model
which predicts Time To Recur using both recurrent and nonrecurrent cases.
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WPBC/
Attribute information
1) ID number
2) Outcome (R = recur, N = nonrecur)
3) Time (recurrence time if field 2 = R, disease-free time if field 2= N)
4-33) Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three largest
values) of these features were computed for each image, resulting in 30
features. For instance, field 4 is Mean Radius, field 14 is Radius SE, field
24 is Worst Radius.
Values for features 4-33 are recoded with four significant digits.
34) Tumor size - diameter of the excised tumor in centimeters
35) Lymph node status - number of positive axillary lymph nodes observed at
time of surgery
My personal Notes :
- I've removed the four instances with unknown values of the last attribute
- I've exchanged the attribute position of attributes n.3 (Time) and n.35
(Lymph node).
- I've removed the attribute outcome as it is the class attribute if the
problem is treated as a classification one.