Package org.nd4j.linalg.dataset.api.preprocessor.classimbalance

Class Summary

Class	Description
BaseUnderSamplingPreProcessor
UnderSamplingByMaskingMultiDataSetPreProcessor	The multidataset version of the UnderSamplingByMaskingPreProcessor Constructor takes a map - keys are indices of the multidataset to apply preprocessor to, values are the target distributions
UnderSamplingByMaskingPreProcessor	For use in time series with unbalanced binary classes trained with truncated back prop through time Undersamples the majority class by randomly masking time steps belonging to it Given a target distribution for the minority class and the window size (usually the value used with tbptt) the preprocessor will approximate the given target distribution for every window of given size for every sample of the minibatch By default '0' is considered the majority class and '1' the minorityLabel class Default can be overriden with .overrideMinorityDefault()