Handle your class imbalance more intelligently by using SMOTE's younger, more sophisticated cousin
Installation is easy. After cloning the project onto your machine and installing the required dependencies,
simply use the
$ git clone https://github.com/tgsmith61591/smrt.git $ cd smrt $ python setup.py install
SMRT (Sythetic Minority Reconstruction Technique) is the new SMOTE (Synthetic Minority Oversampling TEchnique). Using variational auto-encoders, SMRT learns the latent factors that best reconstruct the observations in each minority class, and then generates synthetic observations until the minority class is represented at a user-defined ratio in relation to the majority class size.
SMRT avoids one of SMOTE's greatest risks: In SMOTE, when drawing random observations from whose k-nearest
neighbors to synthetically reconstruct, the possibility exists that a "border point," or an observation very close to
the decision boundary may be selected. This could result in the synthetically-generated observations lying
too close to the decision boundary for reliable classification, and could lead to the degraded performance
of an estimator. SMRT avoids this risk implicitly, as the
learns a distribution that is generalizable to the lowest-error (i.e., most archetypal) observations.
See the paper for more in-depth reference.
The SMRT example is an ipython notebook with reproducible code and data that compares an imbalanced variant of the MNIST dataset after being balanced with both SMOTE and SMRT. The following are several of the resulting images produced from both SMOTE and SMRT, respectively. Even visually, it's evident that SMRT better synthesizes data that resembles the input data.
The MNIST dataset was amended to contain only zeros and ones in an unbalanced (~1:100, respectively) ratio. The top row
are the original MNIST images, the second row is the SMRT-generated images, and the bottom row is the SMOTE-generated