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FEATURE SELECTION PARALLELIZATION BASED ON PRIORITY QUEUE

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 Subject of Research.The paper deals with feature selection algorithms in machine learning and, particularly, in classification. А method for fast feature selection is proposed. This method combines several other feature selection methods into one linear combination (ensemble) and then optimizes their coefficients. Method. Proposed method is a priority queue based method for feature selection. It is an improvement of measure linear form (MeLiF) algorithm. This method uses priority queue for parallelization, and basically is a parallel version of the MeLiF algorithm. Main Results. Proposed and original algorithms were compared by classification quality and computation time. Comparison was performed on 36 open DNA-microarrays. It was shown that both methods had approximately the same classification quality but computation time of the new method is 4.2 to 22 times lower on a 24-core processor with 50 threads. Practical Relevance. Proposed algorithm could be used as one of the main steps in data preprocessing for high dimensional data in machine learning. Therefore, it could be used in a wide specter of classification problems on high-dimensional datasets.

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