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Towards Faster Execution of Ensemble ML Bootstrap Based Techniques.

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Summary
This summary is machine-generated.

This study introduces a new framework to reduce redundant computations in ensemble methods (EM), making them more practical. The approach optimizes algorithms by viewing them as computational units, leading to faster execution plans.

Keywords:
DAGautomatic redundancy reductioncompilerdependence analysismachine learningparallel processingtheory of computation

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Area of Science:

  • Computer Science
  • Machine Learning
  • Algorithm Optimization

Background:

  • Ensemble methods (EM) based on bootstrap aggregation often suffer from redundant computations (RC), limiting their practical application.
  • Existing methods struggle to efficiently manage computational resources for complex algorithms.

Purpose of the Study:

  • To propose a novel framework for reducing redundant computations (RRC) in ensemble methods.
  • To enhance the practicality and execution speed of EM algorithms.

Main Methods:

  • Viewing algorithms as collections of computational units (cu) comprising operations and data.
  • Adapting VLSI floor tiling principles for RRC.
  • Formulating RRC as a variation of the directed bandwidth problem on directed acyclic graphs (DAGs).
  • Introducing a new notion of (r,s) set cover for DAGs in limited memory scenarios.

Main Results:

  • Under unbounded memory, the approach reduces to the directed bandwidth problem.
  • In limited memory scenarios, RRC is formulated as a variation of the directed bandwidth problem.
  • Minimum graph bandwidth is closely related to memory requirements for RRC.
  • Preliminary experiments show the approach's effectiveness for RRC in EM.

Conclusions:

  • The proposed framework effectively reduces redundant computations in ensemble methods.
  • The approach shows promise for broader applications in decision sciences and algorithm optimization.
  • Optimizing computational units and memory management is key to improving EM algorithm efficiency.