  Differences in data size per class, also known as imbalanced data
distribution, have become a common problem affecting data quality. Big Data
scenarios pose a new challenge to traditional imbalanced classification
algorithms, since they are not prepared to work with such amount of data. Split
data strategies and lack of data in the minority class due to the use of
MapReduce paradigm have posed new challenges for tackling the imbalance between
classes in Big Data scenarios. Ensembles have shown to be able to successfully
address imbalanced data problems. Smart Data refers to data of enough quality
to achieve high performance models. The combination of ensembles and Smart
Data, achieved through Big Data preprocessing, should be a great synergy. In
this paper, we propose a novel Smart Data driven Decision Trees Ensemble
methodology for addressing the imbalanced classification problem in Big Data
domains, namely SD_DeTE methodology. This methodology is based on the learning
of different decision trees using distributed quality data for the ensemble
process. This quality data is achieved by fusing Random Discretization,
Principal Components Analysis and clustering-based Random Oversampling for
obtaining different Smart Data versions of the original data. Experiments
carried out in 21 binary adapted datasets have shown that our methodology
outperforms Random Forest.
