Basic characteristics Ballbearing
913 
target objects 
Ball bearing train, downloaded from http://www.sidanet.org/. Task is to distinguish new ballbearning from bearings where outer race is completely broken, broken cage, damaged case and worn bearnings. Acceleration time signals are measured, signal is FFTed resulting in 32 features. Download matfile with Prtools dataset. 
0 
outlier objects 

32 
features 
Unsupervised PCA Ballbearing
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 Ballbearing
On the left, the Fisher scatterplot is shown, on the right the ROC curve along this direction. 
Results Ballbearing
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  99.9 (0.0)  100.0 (0.0)  86.2 (0.0) 
Min.Cov.Determinant  100.0 (0.0)  100.0 (0.0)  86.1 (0.0) 
Mixture of Gaussians  100.0 (0.0)  100.0 (0.0)  87.6 (0.2) 
Naive Parzen  100.0 (0.0)  100.0 (0.0)  86.7 (0.0) 
Parzen  98.7 (0.0)  100.0 (0.0)  86.4 (0.0) 
kmeans  95.0 (0.2)  100.0 (0.0)  82.5 (0.5) 
1Nearest Neighbors  98.7 (0.0)  100.0 (0.0)  86.7 (0.0) 
kNearest Neighbors  98.7 (0.0)  100.0 (0.0)  86.7 (0.0) 
Nearestneighbor dist  97.9 (0.0)  99.8 (0.0)  79.0 (0.0) 
Principal comp.  99.5 (0.0)  99.9 (0.0)  86.2 (0.0) 
SelfOrgan. Map  93.2 (0.7)  100.0 (0.0)  80.1 (1.4) 
Autoenc network  99.2 (0.0)  100.0 (0.0)  84.4 (1.3) 
MST  NaN (0.0)  NaN (0.0)  NaN (0.0) 
L_1ball  99.9 (0.0)  98.4 (0.0)  86.9 (0.0) 
kcenter  96.1 (0.3)  100.0 (0.0)  80.5 (0.5) 
Support vector DD  0.0 (0.0)  98.7 (0.0)  0.0 (0.0) 
Minimax Prob. DD  1.6 (0.0)  100.0 (0.0)  9.2 (0.0) 
LinProg DD  5.2 (1.2)  100.0 (0.0)  17.6 (1.8) 
Lof DD  99.4 (0.0)  99.9 (0.0)  84.4 (0.0) 
Lof range DD  99.2 (0.0)  100.0 (0.0)  86.6 (0.0) 
Loci DD  93.9 (0.0)  100.0 (0.0)  81.5 (0.0) 
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:



