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Attention-based random forest and contamination model.

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A novel attention-based random forest (ABRF) method enhances machine learning models by assigning trainable attention weights to decision trees. This approach improves both regression and classification tasks, offering a new tool for data analysis.

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Attention mechanismContamination modelNadaraya-Watson regressionQuadratic programmingRandom forest

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Random Forests (RF) are powerful ensemble methods for regression and classification.
  • Existing RF models lack mechanisms to dynamically adjust the influence of individual trees.
  • Attention mechanisms offer a way to learn instance-specific feature importance.

Purpose of the Study:

  • To introduce the Attention-Based Random Forest (ABRF) and its modifications.
  • To apply attention mechanisms within the RF framework for enhanced predictive performance.
  • To improve upon standard RF models by incorporating instance-dependent tree weighting.

Main Methods:

  • Developed ABRF by assigning trainable attention weights to decision trees.
  • Attention weights are determined by the distance between instances in a leaf node.
  • Proposed three modifications: Huber's contamination model with optimization, and gradient-based algorithms for parameter training.

Main Results:

  • Numerical experiments demonstrated the effectiveness of ABRF on diverse regression and classification datasets.
  • The proposed modifications showed competitive or superior performance compared to baseline methods.
  • The attention mechanism allowed for more nuanced data representation and prediction.

Conclusions:

  • ABRF provides a flexible and effective extension of the random forest algorithm.
  • The attention-based weighting scheme enhances the adaptability and accuracy of RF models.
  • The publicly available code facilitates further research and application of ABRF.