Package
weka.filters.supervised.instance
Synopsis
Resamples a dataset by applying the Synthetic Minority Oversampling TEchnique (SMOTE). The original dataset must fit entirely in memory. The amount of SMOTE and number of nearest neighbors may be specified. For more information, see
Nitesh V. Chawla et. al. (2002). Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 16:321-357.
Options
The table below describes the options available for SMOTE.
Option |
Description |
|---|---|
classValue |
The index of the class value to which SMOTE should be applied. Use a value of 0 to auto-detect the non-empty minority class. |
nearestNeighbors |
The number of nearest neighbors to use. |
percentage |
The percentage of SMOTE instances to create. |
randomSeed |
The seed used for random sampling. |
Capabilities
The table below describes the capabilites of SMOTE.
Capability |
Supported |
|---|---|
Class |
Nominal class, Binary class, Missing class values |
Attributes |
Binary attributes, String attributes, Nominal attributes, Numeric attributes, Unary attributes, Relational attributes, Date attributes, Missing values, Empty nominal attributes |
Min # of instances |
0 |