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Performing Multi-Target Regression via a Parameter Sharing-Based Deep Network.

Oscar Reyes1,2, Sebastián Ventura1,3,2

  • 1Department of Computer Science and Numerical Analysis, University of Córdoba Rabanales Campus, 14071 Córdoba, Spain.

International Journal of Neural Systems
|June 14, 2019
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This study introduces a new neural network for multi-target regression (MTR). The model effectively handles inter-target dependencies and complex relationships, outperforming existing methods.

Keywords:
Multi-target regressiondeep learninghard parameter sharing

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Multi-target regression (MTR) involves predicting multiple continuous variables simultaneously.
  • Key challenges include modeling inter-target dependencies and complex input-output relationships.

Purpose of the Study:

  • To propose a flexible neural network model for multi-target regression.
  • To address challenges in inter-target dependency exploration and input-output modeling.

Main Methods:

  • A deep neural network architecture designed for multiple continuous outputs.
  • Parameter sharing to model inter-target relationships and non-shared parameters for target-specific patterns.

Main Results:

  • Extensive experiments on 18 datasets demonstrate the model's effectiveness.
  • A shared representation approach successfully exploits commonalities between target variables.

Conclusions:

  • The proposed neural network model is competitive with state-of-the-art MTR methods.
  • The model offers a flexible and effective solution for multi-target regression problems.