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Sequential selection of variables using short permutation procedures and multiple adjustments: An application to

Marcelo Azevedo Costa1, Thiago de Souza Rodrigues2, André Gabriel Fc da Costa3

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Summary

This study introduces a new method for selecting important variables in classification tasks with many predictors and few samples. The approach enhances model performance, particularly for genomic data analysis.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • Classification problems with high-dimensional data (many predictors, few samples) pose significant challenges.
  • Variable selection is crucial for building accurate and interpretable models in such scenarios.
  • Existing methods may struggle with computational efficiency and performance in genomic datasets.

Purpose of the Study:

  • To propose a novel sequential methodology for variable selection in high-dimensional classification.
  • To enhance computational efficiency through a new parametric distribution.
  • To develop compact classification models with improved predictive performance for genomic data.

Main Methods:

  • A sequential variable selection methodology is introduced.
  • A Monte Carlo permutation procedure is employed for hypothesis testing.
  • A new parametric distribution, the Truncated and Zero Inflated Gumbel Distribution, is proposed for computational improvement.

Main Results:

  • The proposed methodology effectively selects compact classification models.
  • Optimized classification performances were achieved on real-world genomic datasets.
  • The new distribution improved computational aspects of the variable selection process.

Conclusions:

  • The developed sequential methodology offers an effective solution for variable selection in high-dimensional classification.
  • The proposed Truncated and Zero Inflated Gumbel Distribution enhances computational efficiency.
  • This approach yields compact and high-performing classification models, especially beneficial for genomic data analysis.