Search of algorithms ensemble-that is, best algorithms combination is common used approach in machine learning. MeLiF algorithm uses this technique for filter feature selection. In our research we proposed parallel version of this algorithm and showed that it is not only improves algorithm performance significantly, but also improves feature selection quality.
One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different ways to improve MeLiF algorithm performance with parallelization. Experiments show that proposed schemes significantly improves algorithm performance and increase feature selection quality.
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