Abstract
Large-scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, where it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that, distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). The proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster. After, we have selected the top ranked feature from each cluster. The top scored features from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the proposed BPSO based on MapReduce which provides an optimized feature subset. The proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.