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Neural Networks Multiobjective Learning With Spherical Representation of Weights.

Honovan P Rocha, Marcelo A Costa, Antonio P Braga

    IEEE Transactions on Neural Networks and Learning Systems
    |January 7, 2020
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a new spherical representation for artificial neural networks (ANNs), simplifying multiobjective learning. This novel approach improves Pareto set estimation and outperforms existing methods for ANNs.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Neuroscience

    Background:

    • Multiobjective learning in artificial neural networks (ANNs) is typically a constrained optimization problem.
    • Existing methods require significant computational resources to maintain constraints, limiting flexibility.
    • The complexity of multiobjective optimization hinders the application of various nonlinear optimization techniques.

    Purpose of the Study:

    • To present a novel spherical representation for ANNs.
    • To simplify the multiobjective learning problem by transforming it into an unconstrained optimization problem.
    • To enable the use of diverse nonlinear optimization methods for ANN training.

    Main Methods:

    • Projecting ANN weights into a novel spherical space defined by radius and angles.

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  • Reformulating the constrained multiobjective learning problem as an unconstrained one.
  • Applying standard nonlinear optimization techniques to the spherical representation.

    • Main Results:

    • The proposed spherical representation simplifies the formulation and reduces computational effort for multiobjective learning.
    • It yields more accurate estimates of the Pareto set compared to classical multiobjective approaches.
    • The method effectively selects solutions from the Pareto set, outperforming state-of-the-art techniques on multiple datasets.

    Conclusions:

    • The spherical representation offers a computationally efficient and flexible alternative for multiobjective ANN learning.
    • This novel approach enhances the accuracy of Pareto set estimation and solution selection.
    • The method demonstrates superior performance over existing techniques in practical applications.