MeLiF+: Optimization of Filter Ensemble Algorithm with Parallel Computing

Isaev, Ilya; Smetannikov, Ivan

doi:10.1007/978-3-319-44944-9_29

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2016

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“…The simplest parallelization scheme called MeLiF+ is described in (Isaev and Smetannikov, 2016). The main disadvantage of this naïve scheme is that it does not scale well.…”

Section: Introductionmentioning

confidence: 99%

New Approaches to Parallelization in Filters Aggregation Based Feature Selection Algorithms

Smetannikov

Isaev

Filchenkov

2016

Procedia Computer Science

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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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“…The simplest parallelization scheme called MeLiF+ is described in (Isaev and Smetannikov, 2016). The main disadvantage of this naïve scheme is that it does not scale well.…”

Section: Introductionmentioning

confidence: 99%