TY - GEN
T1 - Optimization networks for integrated machine learning
AU - Kommenda, Michael
AU - Karder, Johannes
AU - Beham, Andreas
AU - Burlacu, Bogdan
AU - Kronberger, Gabriel
AU - Wagner, Stefan
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Feature selection
KW - Machine learning
KW - Optimization analysis
KW - Optimization networks
UR - http://www.scopus.com/inward/record.url?scp=85041852289&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-74718-7_47
DO - 10.1007/978-3-319-74718-7_47
M3 - Conference contribution
SN - 9783319747170
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 399
BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
A2 - Moreno-Diaz, Roberto
A2 - Quesada-Arencibia, Alexis
A2 - Pichler, Franz
PB - Springer
T2 - 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Y2 - 19 February 2017 through 24 February 2017
ER -