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Feature Selection Based on a Large-Scale Many-Objective Evolutionary Algorithm.

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Summary
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This study introduces a new evolutionary algorithm, MALSMEA, to solve complex feature selection problems. MALSMEA improves efficiency and convergence for optimizing feature selection models.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Optimization

Background:

  • Feature selection is crucial across research fields, often treated as an optimization challenge.
  • Traditional evolutionary algorithms struggle with the complexity of large-scale, many-objective feature selection.
  • A multi-objective model considering feature count, accuracy, relevance, redundancy, and class distances is proposed.

Purpose of the Study:

  • To address the limitations of traditional algorithms in large-scale, many-objective feature selection.
  • To propose a novel evolutionary algorithm, MALSMEA, for optimizing complex feature selection models.
  • To enhance the efficiency and convergence of evolutionary algorithms for feature selection.

Main Methods:

  • A large-scale many-objective feature selection model is constructed.
  • A modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA) is proposed.
  • MALSMEA incorporates polynomial mutation with variable grouping and a novel worst-case solution replacement strategy using shift-based density estimation.

Main Results:

  • MALSMEA demonstrates competitive performance in optimizing the proposed feature selection model.
  • The algorithm shows effectiveness in handling large-scale, many-objective optimization problems.
  • Improvements in efficiency and convergence are achieved compared to traditional methods.

Conclusions:

  • The proposed MALSMEA algorithm is a viable and effective approach for complex feature selection.
  • The novel strategies within MALSMEA enhance its capability for multi-objective optimization.
  • This work contributes to advancing evolutionary algorithms for practical feature selection applications.