Imbalanced Big Data Classification: A Distributed Implementation of SMOTE

In the domain of machine learning, quality of data is most critical component for building good models. Predictive analytics is an AI stream used to predict future events based on historical 'earnings and is used in diverse fields like predicting online frauds, oil slicks, intrusion attacks, credit defaults, prognosis of disease cells etc. Unfortunately, in most of these cases, traditional learning models fail to generate required results due to imbalanced nature of data. Here imbalance denotes small number of instances belonging to the class under prediction like fraud instances in the total online transactions. The prediction in imbalanced classification gets further limited due to factors like small disjuncts which get accentuated during the partitioning of data when learning at scale. Synthetic generation of minority class data (SMOTE [(1) under bar]) is one pioneering approach by Chawla [(1) under bar] to offset said limitations and generate more balanced datasets. Although there exists a standard implementation of SMOTE in python, it is unavailable for distributed computing environments for large datasets. Bringing SMOTE to distributed environment under spark is the key motivation for our research. In this paper we present our algorithm, observations and results for synthetic generation of minority class data under spark using Locality Sensitivity Hashing [LSH]. We were able to successfully demonstrate a distributed version of Spark SMOTE which generated quality artificial samples preserving spatial distribution(1).