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Distributed Function Mining for Gene Expression Programming Based on Fast Reduction

Author

Listed:
  • Song Deng
  • Dong Yue
  • Le-chan Yang
  • Xiong Fu
  • Ya-zhou Feng

Abstract

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.

Suggested Citation

  • Song Deng & Dong Yue & Le-chan Yang & Xiong Fu & Ya-zhou Feng, 2016. "Distributed Function Mining for Gene Expression Programming Based on Fast Reduction," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0146698
    DOI: 10.1371/journal.pone.0146698
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