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Bagged k-nearest neighbours classification with uncertainty in the variables
Joe L Villa Medina1, Ricard Boqué, Joan Ferré
1Department of Analytical Chemistry and Organic Chemistry, Rovira i Virgili University, C/Marcel.lí Domingo, s/n. 43007 Tarragona, Catalonia, Spain.
Analytica Chimica Acta
|June 16, 2009
Summary
This study introduces a novel method for supervised classification that incorporates experimental uncertainty. By combining k-nearest neighbours (kNN) with a nested bootstrap scheme, it enhances classification reliability, outperforming existing methods on the Wine dataset.
Area of Science:
- Machine Learning
- Data Science
- Statistical Modeling
Background:
- Supervised classification typically ignores experimental result uncertainty.
- Integrating uncertainty information can improve classification reliability.
Purpose of the Study:
- To propose a novel method for supervised classification that accounts for experimental uncertainty.
- To enhance the reliability of classification by incorporating uncertainty data.
Main Methods:
- A nested bootstrap scheme combining classical bootstrap and a novel U-bootstrap method.
- Utilizing k-nearest neighbours (kNN) with BxD new training bootstrap sets.
- Computing classification reliability based on uncertainty and object position relative to training data.
Main Results:
- The proposed method's classification reliability adjusts with increased uncertainty.
- Achieved a classification error rate (CER) comparable to kNN (5.57%) on the Wine dataset.
- Demonstrated lower CER than Probabilistic Bagged k-nearest neighbours (PBkNN) using Hamamoto's (7.96%) or Efron's (8.97%) bootstrap.
Conclusions:
- The novel approach effectively integrates experimental uncertainty into supervised classification.
- The method offers improved classification reliability and competitive error rates.
- This technique provides a more robust classification framework by considering data uncertainty.
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