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license: cc-by-sa-4.0 |
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## Breast Cancer Wisconsin Diagnostic Dataset |
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Following description was retrieved from [breast cancer dataset on UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)). |
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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 [here](https://pages.cs.wisc.edu/~street/images/). |
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Separating plane described above was obtained using Multisurface Method-Tree (MSM-T), 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. |
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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]. |
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Attribute Information: |
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- ID number |
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- Diagnosis (M = malignant, B = benign) |
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Ten real-valued features are computed for each cell nucleus: |
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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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