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WisconsinPrognosticBreastCancer Class |
Inheritance HierarchyAccord.DataSetsWisconsinPrognosticBreastCancer
Namespace: Accord.DataSets
Assembly: Accord.DataSets (in Accord.DataSets.dll) Version: 3.8.0
SyntaxThe WisconsinPrognosticBreastCancer type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | WisconsinPrognosticBreastCancer |
Prepares the Wisconsin's (Diagnostic) Breast Cancer dataset.
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Properties| Name | Description | |
|---|---|---|
 | ClassLabels |
Gets the class labels associated with each instance
in Wisconsin's Breast Cancer dataset.
|
| ClassNames |
Gets the outcome labels in Wiscosin's Prognostic Breast Cancer dataset:
"Recurring" (cancer has re-orcurred during observation period), "Nonrecurring" (disease-free during observation period).
|
| Features |
Gets the data instance features contained in Wisconsin's Breast Cancer dataset.
|
| InstanceIds |
Gets identifiers for each of the instances contained in Wisconsin's Breast Cancer dataset.
|
| Outcome |
Gets the outcome for each of the data instances in the dataset. The outcomes can be either
0 (Recurring) or 1 (Non-recurring) depending on what happened at the time instants stored
in Time.
|
| Time |
Gets the time until a possible Outcome has been detected. If the outcome has been
ruled as recurring (0), it means that the cancer reocurred after the given number of days after
the initial treatment performed for this patient. If the outcome is marker as non-recurring (1),
it means the cancer has not been observed until the given number of days marked in this array.
|
| VariableNames |
Gets the variable names in Wisconsin's Breast Cancer dataset:
"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)
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Methods| Name | Description | |
|---|---|---|
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object.) |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.) |
| GetHashCode | Serves as the default hash function. (Inherited from Object.) |
| GetType | Gets the Type of the current instance. (Inherited from Object.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| ToString | Returns a string that represents the current object. (Inherited from Object.) |
Extension Methods| Name | Description | |
|---|---|---|
 | HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) |
| IsEqual |
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.) |
| To(Type) | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
| ToT | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
RemarksEach 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.See references (i) and (ii) above for details of the RSA method.
References:
See Also