Gallery
In this page, we demonstrate the output of various oversampling and noise removal techniques, using default parameters.
For binary oversampling and nosie removal, an artificial database was used, available in the datasets module.
Oversampling sample results
In the captions of the images some abbreviations referring to the operating principles are placed. Namely:
NR: noise removal is involved
DR: dimension reduction is applied
Clas: some supervised classifier is used
SCmp: sampling is carried out componentwise (attributewise)
SCpy: sampling is carried out by copying instances
SO: ordinary sampling (just like in SMOTE)
M: memetic optimization is used
DE: density estimation is used
DB: density based - the sampling is based on a density of importance assigned to the instances
Ex: the sampling is extensive - samples are added successively, not optimizing the holistic distribution of a given number of samples
CM: changes majority - even majority samples can change
Clus: uses some clustering technique
BL: identifies and samples the neighborhoods of borderline samples
A: developed for a specific application