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Towards parallel migrating birds framework for feature subset problem

Published: 01 October 2018 Publication History

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Abstract

Migrating birds optimization is of the most promising population based techniques applied to many real world applications in particular the feature subset problem. The sequential version of feature selection based on migrating birds optimization achieves good results but consumes a high computational time. To tackle this problem, we propose a parallel population feature selection approach based on migrating birds optimization (MBO) adapted to big data systems. This approach follows the paradigm of MapReduce and we describe different phases to implement it using Hadoop platform.

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Cited By

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  • (2023)Solving a new application of asymmetric TSP by modified migrating birds optimization algorithmEvolutionary Intelligence10.1007/s12065-023-00858-817:3(1697-1713)Online publication date: 6-Jul-2023

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cover image ACM Other conferences
DATA '18: Proceedings of the First International Conference on Data Science, E-learning and Information Systems
October 2018
274 pages
ISBN:9781450365369
DOI:10.1145/3279996
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 01 October 2018

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  1. feature selection
  2. migrating birds optimization
  3. optimization techniques
  4. parallel computing

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  • (2023)Solving a new application of asymmetric TSP by modified migrating birds optimization algorithmEvolutionary Intelligence10.1007/s12065-023-00858-817:3(1697-1713)Online publication date: 6-Jul-2023

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