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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.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (SMOTE).

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

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