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Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulations.

Yadi Wang1, Jun Wang2, Hangjun Che3

  • 1Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 475004, China; Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China.

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Summary
This summary is machine-generated.

This study introduces a novel neurodynamics approach for supervised feature selection, overcoming fractional programming complexity. The method achieves global convergence and superior classification performance on benchmark datasets.

Keywords:
Feature selectionNeurodynamic optimizationRecurrent neural networksTwo-timescale neurodynamics

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Area of Science:

  • Machine Learning
  • Computational Neuroscience
  • Optimization

Background:

  • Feature selection is vital for effective data processing and machine learning.
  • Existing methods often employ greedy or sequential strategies.
  • A recent neurodynamics approach uses fractional programming for simultaneous feature redundancy minimization and relevance maximization.

Purpose of the Study:

  • To reformulate the fractional programming problem into equivalent bilevel and bilinear programming problems.
  • To address the complexity arising from the fractional gradient of the objective function.
  • To develop and evaluate novel neurodynamic models for supervised feature selection.

Main Methods:

  • Reformulation of fractional programming into bilevel and bilinear programming.
  • Adaptation of two two-timescale projection neural networks.
  • Solving reformulated problems using the proposed neural networks.

Main Results:

  • Demonstrated global convergence of the neurodynamic approaches.
  • Achieved high classification performance on six benchmark datasets.
  • Outperformed six mainstream feature selection methods.

Conclusions:

  • The proposed neurodynamic approaches effectively solve the reformulated feature selection problems.
  • The methods offer a viable alternative to fractional programming for supervised feature selection.
  • The study highlights the potential of neurodynamics in optimizing feature selection tasks.