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GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning.

Carmen Esposito1, Gregory A Landrum1,2, Nadine Schneider3

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Summary
This summary is machine-generated.

Optimizing machine learning classification thresholds improves predictions for imbalanced datasets without retraining models. New automated methods, including GHOST, enhance performance in drug discovery by better identifying minority classes.

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Area of Science:

  • * Computational chemistry and cheminformatics.
  • * Machine learning and artificial intelligence in drug discovery.
  • * Bioinformatics and data science for pharmaceutical research.

Background:

  • * Machine learning classifiers struggle with imbalanced data, often overpredicting the majority class.
  • * This imbalance leads to higher misclassification rates for minority classes, which are frequently the targets of interest in drug discovery.
  • * The default 0.5 classification threshold is suboptimal for imbalanced datasets.

Purpose of the Study:

  • * To introduce two automated procedures for selecting optimal decision thresholds in imbalanced classification.
  • * To demonstrate that these methods do not require model retraining or data resampling.
  • * To evaluate the effectiveness of these thresholding strategies in drug discovery applications.

Main Methods:

  • * Development of a Random Forest (RF)-specific automated threshold selection procedure.
  • * Introduction of the GHOST (General Heuristic Optimization of Thresholds) procedure, applicable to various machine learning classifiers.
  • * Validation on 138 public drug discovery datasets with structure-activity relationship data.

Main Results:

  • * Both automated thresholding methods significantly improved RF performance.
  • * The GHOST procedure, tested with four classifiers and two molecular descriptors, benefited most classifiers.
  • * GHOST outperformed other strategies like random undersampling and conformal prediction.
  • * Thresholding procedures proved effective even when training and test data characteristics varied.

Conclusions:

  • * Automated decision threshold optimization is a powerful strategy for improving imbalanced classification in drug discovery.
  • * The GHOST method offers a versatile and effective approach to address class imbalance without altering the underlying machine learning models.
  • * These thresholding techniques have practical implications for real-world drug discovery projects facing diverse data challenges.