Optimization Networks for Integrated Machine Learning
This work addresses the problem of integrated optimization in machine learning for researchers and practitioners, but it appears incremental as it adapts an existing methodology to new domains.
The paper tackles the challenge of solving interrelated machine learning problems by applying optimization networks, a methodology originally for combinatorial optimization, to tasks like feature selection and linear model creation, showing advantages over ordinary least squares with elastic net regularization.
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.