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Distributed Function Mining for Gene Expression Programming Based on Fast Reduction

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene e...

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Detalles Bibliográficos
Autores principales: Deng, Song, Yue, Dong, Yang, Le-chan, Fu, Xiong, Feng, Ya-zhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709085/
https://www.ncbi.nlm.nih.gov/pubmed/26751200
http://dx.doi.org/10.1371/journal.pone.0146698
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author Deng, Song
Yue, Dong
Yang, Le-chan
Fu, Xiong
Feng, Ya-zhou
author_facet Deng, Song
Yue, Dong
Yang, Le-chan
Fu, Xiong
Feng, Ya-zhou
author_sort Deng, Song
collection PubMed
description For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining.
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spelling pubmed-47090852016-01-15 Distributed Function Mining for Gene Expression Programming Based on Fast Reduction Deng, Song Yue, Dong Yang, Le-chan Fu, Xiong Feng, Ya-zhou PLoS One Research Article For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. Public Library of Science 2016-01-11 /pmc/articles/PMC4709085/ /pubmed/26751200 http://dx.doi.org/10.1371/journal.pone.0146698 Text en © 2016 Deng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Deng, Song
Yue, Dong
Yang, Le-chan
Fu, Xiong
Feng, Ya-zhou
Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title_full Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title_fullStr Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title_full_unstemmed Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title_short Distributed Function Mining for Gene Expression Programming Based on Fast Reduction
title_sort distributed function mining for gene expression programming based on fast reduction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709085/
https://www.ncbi.nlm.nih.gov/pubmed/26751200
http://dx.doi.org/10.1371/journal.pone.0146698
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