Basic characteristics Biomed (targetcl. carrier)
67 
target objects 
The purpose of the analysis is to develop a screening procedure to detect carriers and to describe its effectiveness. Entries with missing values have been removed. Download matfile with Prtools dataset. 
127 
outlier objects 

5 
features 
Unsupervised PCA Biomed (targetcl. carrier)
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 Biomed (targetcl. carrier)
On the left, the Fisher scatterplot is shown, on the right the ROC curve along this direction. 
Results Biomed (targetcl. carrier)
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  62.1 (1.0)  60.0 (1.4)  63.4 (0.5) 
Min.Cov.Determinant  53.4 (0.9)  53.4 (0.9)  56.0 (0.9) 
Mixture of Gaussians  42.6 (1.5)  46.7 (2.0)  43.1 (1.4) 
Naive Parzen  53.4 (0.9)  53.4 (0.9)  50.4 (0.8) 
Parzen  38.5 (1.2)  49.1 (0.8)  48.4 (0.4) 
kmeans  40.6 (2.9)  56.0 (2.9)  41.0 (1.9) 
1Nearest Neighbors  27.2 (0.8)  46.7 (1.2)  25.0 (0.9) 
kNearest Neighbors  27.2 (0.8)  46.7 (1.2)  25.0 (0.9) 
Nearestneighbor dist  44.4 (2.6)  57.5 (1.6)  42.1 (1.7) 
Principal comp.  62.6 (1.1)  42.3 (1.3)  59.9 (3.5) 
SelfOrgan. Map  68.4 (2.1)  46.5 (3.0)  67.9 (1.0) 
Autoenc network  51.9 (5.3)  54.6 (2.5)  45.0 (3.3) 
MST  31.9 (1.1)  47.1 (1.7)  33.7 (1.2) 
L_1ball  66.9 (0.6)  73.2 (1.5)  64.0 (0.9) 
kcenter  32.1 (4.5)  41.4 (4.9)  27.8 (2.6) 
Support vector DD  50.0 (0.0)  42.9 (2.2)  41.6 (0.9) 
Minimax Prob. DD  23.9 (0.5)  51.6 (1.4)  24.3 (0.8) 
LinProg DD  25.4 (0.6)  64.6 (0.8)  24.4 (1.0) 
Lof DD  49.4 (2.5)  50.4 (2.5)  45.8 (2.4) 
Lof range DD  41.9 (2.0)  53.0 (1.5)  41.6 (1.4) 
Loci DD  49.4 (1.2)  50.2 (1.3)  48.0 (0.8) 
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:



