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Distributed Function Mining for Gene Expression Programming Based on Fast Reduction.

Song Deng1, Dong Yue1, Le-chan Yang2

  • 1Institute of Advanced Technology, Nanjing University Post & Telecommunication, Nanjing, 210023, China.

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|January 12, 2016
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Summary
This summary is machine-generated.

This study introduces distributed function mining for gene expression programming (DFMGEP-FR), an improved algorithm that significantly reduces runtime and enhances prediction accuracy for large datasets. DFMGEP-FR outperforms traditional methods and parallel approaches in efficiency and capability.

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Data Mining

Background:

  • Traditional gene expression programming (GEP) faces challenges with high-dimensional and massive datasets, leading to increased runtime and reduced prediction accuracy.
  • Existing improved algorithms also struggle to efficiently handle the complexity of large-scale data.
  • There is a need for novel algorithms that can effectively process and mine functions from extensive datasets.

Purpose of the Study:

  • To propose a new improved algorithm, distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR), to address the limitations of traditional GEP.
  • To enhance prediction accuracy and reduce computational runtime for high-dimensional and massive data sets.
  • To demonstrate the efficiency and capability of DFMGEP-FR in distributed function mining.

Main Methods:

  • Developed DFMGEP-FR, incorporating fast attribution reduction in binary search algorithms (FAR-BSA) for optimal attribution set identification.
  • Implemented a function consistency replacement algorithm for integrating local function models.
  • Conducted comparative experiments against centralized GEP and parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA).

Main Results:

  • DFMGEP-FR demonstrated significant reductions in average time-consumption: 89.09%-93.06% compared to centralized GEP.
  • Compared to parallel GEPSA, DFMGEP-FR showed time-consumption reductions of 8.42%-13.75% across various datasets.
  • Experiments on six well-studied UCI datasets validated the efficiency and capability of DFMGEP-FR for distributed function mining.

Conclusions:

  • DFMGEP-FR effectively overcomes the runtime and accuracy limitations of traditional GEP for large-scale datasets.
  • The proposed algorithm offers a more efficient and capable solution for distributed function mining.
  • DFMGEP-FR represents a significant advancement in processing high-dimensional and massive data for gene expression programming applications.