  Feature selection (FS) is a key research area in the machine learning and
data mining fields, removing irrelevant and redundant features usually helps to
reduce the effort required to process a dataset while maintaining or even
improving the processing algorithm's accuracy. However, traditional algorithms
designed for executing on a single machine lack scalability to deal with the
increasing amount of data that has become available in the current Big Data
era. ReliefF is one of the most important algorithms successfully implemented
in many FS applications. In this paper, we present a completely redesigned
distributed version of the popular ReliefF algorithm based on the novel Spark
cluster computing model that we have called DiReliefF. Spark is increasing its
popularity due to its much faster processing times compared with Hadoop's
MapReduce model implementation. The effectiveness of our proposal is tested on
four publicly available datasets, all of them with a large number of instances
and two of them with also a large number of features. Subsets of these datasets
were also used to compare the results to a non-distributed implementation of
the algorithm. The results show that the non-distributed implementation is
unable to handle such large volumes of data without specialized hardware, while
our design can process them in a scalable way with much better processing times
and memory usage.
