Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data

TL;DR

This paper introduces the Diversified Multiple Tree (DMT) ensemble classifier, which is more robust to noise in biomedical data than other ensemble methods, demonstrating higher accuracy on real-world noisy datasets.

Contribution

The paper presents DMT, a novel ensemble classifier designed for high-dimensional noisy biomedical data, showing improved robustness and accuracy over existing methods.

Findings

DMT outperforms benchmark ensemble classifiers on noisy biomedical datasets.

DMT demonstrates significantly higher accuracy in noisy conditions.

Discussion of DMT's limitations and potential variations.

Abstract

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble classifiers. Experimental results show that DMT is significantly more accurate than other benchmark ensemble classifiers on noisy test data. We also discuss a limitation of DMT and its possible variations.