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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.

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.