Basic characteristics Survival >5yr
225 
target objects 
Haberman's Survival Data from UCI. The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer. The class with more than 5 year survival is used as target class. Download matfile with Prtools dataset. 
81 
outlier objects 

3 
features 
Unsupervised PCA Survival >5yr
On the left, the PCA scatterplot is shown, on the right the retained variance for varying number of features.  
On the left, the PCA scatterplot is shown of data rescaled to unit variance, on the right the retained variance. 
Supervised Fisher Survival >5yr
On the left, the Fisher scatterplot is shown, on the right the ROC curve along this direction. 
Results Survival >5yr
The experiments are performed using dd_tools. A rudimentary explanation of the classifiers is given in the classifier section.
Classifiers  Preproc  

none  unit var  PCA 95\%  
Gauss  59.2 ( 2.4)  60.1 ( 2.8)  59.2 ( 2.4) 
Min.Cov.Determinant  69.5 ( 0.6)  69.5 ( 0.6)  69.5 ( 0.6) 
Mixture of Gaussians  65.2 ( 1.9)  65.2 ( 2.4)  65.1 ( 2.0) 
Naive Parzen  64.8 ( 1.3)  64.8 ( 1.3)  67.5 ( 2.4) 
Parzen  66.5 ( 1.2)  65.3 ( 2.6)  66.5 ( 1.2) 
kmeans  64.9 ( 2.6)  62.3 ( 3.0)  65.4 ( 1.6) 
1Nearest Neighbors  50.8 ( 2.9)  50.2 ( 3.2)  48.8 ( 2.5) 
kNearest Neighbors  50.8 ( 2.9)  50.2 ( 3.2)  48.8 ( 2.5) 
knn, optAUC  49.8 ( 4.4)  52.2 ( 2.8)  49.2 ( 4.8) 
Nearestneighbor dist  47.2 ( 4.0)  49.6 ( 2.5)  49.0 ( 4.7) 
Principal comp.  53.9 ( 0.9)  47.2 ( 3.9)  53.9 ( 0.9) 
SelfOrgan. Map  62.8 ( 2.2)  62.1 ( 2.8)  62.8 ( 2.2) 
Autoenc network  59.3 ( 4.4)  59.7 ( 1.6)  60.1 ( 2.4) 
Spanning Tree  49.0 ( 5.0)  49.8 ( 4.3)  49.9 ( 5.9) 
L_1ball  51.0 ( 1.1)  49.0 ( 0.6)  57.2 ( 1.0) 
kcenter  47.3 ( 2.5)  56.1 ( 4.6)  52.8 ( 0.5) 
Support vector DD  53.4 ( 2.7)  58.4 ( 0.9)  52.5 ( 3.4) 
Minimax Prob. DD  66.9 ( 0.7)  53.3 ( 0.5)  66.9 ( 0.7) 
LinProg DD  63.0 ( 3.5)  55.0 ( 2.8)  62.4 ( 3.6) 
Classifier projection spaces The first classifier projection spaces are obtained by computing the classifier label disagreements (setting the threshold on 10% target error) and applying an MDS on the resulting distance matrix between classifiers:




Classifier projection spaces The second versions of the classifier projection spaces are obtained by computing the classifier ranking disagreements and applying an MDS on the resulting distance matrix between classifiers:



