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Optimization Networks for Integrated Machine Learning

  • Michael KommendaEmail author
  • Johannes Karder
  • Andreas Beham
  • Bogdan Burlacu
  • Gabriel Kronberger
  • Stefan Wagner
  • Michael Affenzeller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)

Abstract

Optimization networks are a new methodology for holistically solving interrelated problems that have been developed with combinatorial optimization problems in mind. In this contribution we revisit the core principles of optimization networks and demonstrate their suitability for solving machine learning problems. We use feature selection in combination with linear model creation as a benchmark application and compare the results of optimization networks to ordinary least squares with optional elastic net regularization. Based on this example we justify the advantages of optimization networks by adapting the network to solve other machine learning problems. Finally, optimization analysis is presented, where optimal input values of a system have to be found to achieve desired output values. Optimization analysis can be divided into three subproblems: model creation to describe the system, model selection to choose the most appropriate one and parameter optimization to obtain the input values. Therefore, optimization networks are an obvious choice for handling optimization analysis tasks.

Keywords

Optimization networks Machine learning Feature selection Optimization analysis 

Notes

Acknowledgments

The authors gratefully acknowledge financial support by the Austrian Research Promotion Agency (FFG) and the Government of Upper Austria within the COMET Project #843532 Heuristic Optimization in Production and Logistics (HOPL).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Michael Kommenda
    • 1
    • 2
    Email author
  • Johannes Karder
    • 1
    • 2
  • Andreas Beham
    • 1
    • 2
  • Bogdan Burlacu
    • 1
    • 2
  • Gabriel Kronberger
    • 1
  • Stefan Wagner
    • 1
  • Michael Affenzeller
    • 1
    • 2
  1. 1.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler University LinzLinzAustria

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