A distributed wrapper approach for feature selection

In recent years, distributed learning has been the focus of much attention due to the proliferation of big databases, usually distributed. In this context, machine learning can take advantage of feature selection methods to deal with these datasets of high dimensionality. However, the great majority of current feature selection algorithms are designed for centralized learning. To confront the problem of distributed feature selection, in this paper we propose a distributed wrapper approach. In this manner, the learning accuracy can be improved, as well as obtaining a reduction in the memory requirements and execution time. Four representative datasets were selected to test the approach, paving the way to its application over extremely-high data which prevented previously the use of wrapper approaches.