SMOTE-MR

SMOTE-MR: A distributed Synthetic Minority Oversampling Technique (SMOTE) [1] for Big Data which applies a MapReduce based-approach. SMOTE-MR is categorized as an approximated/ non exact solution. Also, there is an exact solution called SMOTE-BD written by the author (See: https://github.com/majobasgall/smote-bd)

How to run it?

A generic example to run it could be:

spark-submit --master "URL" --executor-memory "XG" "path-to-jar".jar --class "path-to-main" --datasetName="aName" --headerFile="path-to-header" --inputFile="path-to-input" --delimiter=", " --outputPah="path-to-output" --seed="aSeed" --K="number-of-neighbours" --numPartitions="number-of-parts" --nReducers="number-of-reducers" --numIterations="number-of-iterations" --minClassName="min-class-name" -overPercentage=100

• Parameters of spark: --master "URL" | --executor-memory "XG" . They can be useful for launch with diferent settings and datasets.
• --class path.to.the.main aJarFile.jar Determine the jar file to be run.
• datasetName The name of the current dataset.
• headerFile Full path to header file.
• inputFile Full path to input file.
• delimiter Delimiter of each attribute value.
• outputPah Full path to output directory.
• seed A seed to generate random numbers.
• K Number of nearest neighbours.
• numPartitions Number of partitions to split data.
• nReducers Number of reducers (required by the K-NN stage).
• numIterations Number of iterations (required by the K-NN stage).
• minClassName Name of the minority class (according to the header file).
• overPercentage Percentage of balancing between classes.

References

[1] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Int. Res., 16(1), 321–357.