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Stability selection for LASSO with weights based on AUC.

Yonghan Kwon1, Kyunghwa Han2, Young Joo Suh2

  • 1Department of Biostatistics and Computing, Yonsei University Graduate School, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.

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Weighted stability selection enhances variable selection by incorporating model performance metrics like AUC. This method offers intuitive parameter settings and improved predictive accuracy, particularly in radiomics and speech signal analysis.

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Stability selection is a resampling-based algorithm for identifying relevant variables in datasets.
  • Existing methods may not fully leverage predictive performance for variable selection.
  • Optimizing variable selection is crucial for building robust predictive models.

Purpose of the Study:

  • To introduce Weighted Stability Selection, an advancement over traditional stability selection.
  • To evaluate the performance and predictive ability of the proposed method.
  • To demonstrate the method's utility in complex datasets like radiomics and speech signals.

Main Methods:

  • Developed Weighted Stability Selection, assigning weights based on Area Under the Curve (AUC) from additional modeling.
  • Conducted extensive simulations to assess True Positive Rate (TPR), Positive Predictive Value (PPV), and selection stability.
  • Validated the method's predictive performance on unseen data and applied it to radiomics and speech signal datasets.

Main Results:

  • Weighted Stability Selection demonstrated comparable performance to standard stability selection in terms of TPR, PPV, and stability.
  • The proposed method achieved a consistently higher AUC in specific scenarios when using selected variables.
  • Application to radiomics and speech signal datasets resulted in higher AUC with fewer selected variables.

Conclusions:

  • Weighted Stability Selection offers an intuitive and effective approach to variable selection.
  • The method enhances predictive performance, particularly in high-dimensional or complex data.
  • It provides a valuable tool for researchers seeking robust variable selection with improved model accuracy.