Smote Upsampling
Synopsis
This operator implements the Synthetic Minority Over-sampling Technique as proposed by Chawla et al., Journal of Artificial Intelligence Research 16 (2002), 321 -- 357.
Description
In the first step the ExampleSet is filtered to only consider examples of the minority class. Afterwards a search on the k nearest neighbours for all examples is performed. The algorithm then selects a random example and a random nearest neighbour for this example. A new example is created which is on the line between the two examples.
Input
exa
ExampleSet you want to upsample.
Output
ups
The original ExampleSet with the attached synthetic examples.
ori
The original ExampleSet.
Parameters
Number of neighbours
In SMOTE we calculate the k nearest neighborhood. This parameter defines the number of neighbors to consider.
Normalize
If checked range transformation to [0,1] is performed to make distance calculation solid.
Equalize classes
If activated as many new examples as needed to balance the classes are drawn.
Upsampling size
Defines the number of examples you want to create.
Auto detect minority class
If activated the class to upsample is the class with the least occurrences.
Minority class
Defines the class you want to upsample.
Round integers
Round Integer attributes to the next Integer.
Nominal change rate
Probability to change a nominal value to the nominal value of it's nearest neighbor.
Append to original
If set to true the new data will be appended to the original data. If set to false only synthetic data is returned.
Use local random seed
This parameter indicates if a local random seed should be used.
Local random seed
If the use local random seed parameter is checked this parameter determines the local random seed.