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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data.

Jiuyong Li¹, Lin Liu¹, Jixue Liu¹

¹School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia.

Health Information Science and Systems

|October 25, 2017

Summary

This summary is machine-generated.

Diversified Multiple Tree (DMT) classification models show superior accuracy in classifying noisy biomedical data from new laboratories. This robust ensemble method outperforms traditional classifiers when data deviates from training sets.

Keywords:

Decision tree Diversified Multiple Tree Ensemble classifier Noisy data Robustness

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Area of Science:

Biomedical data analysis
Machine learning in healthcare
Computational biology

Background:

Classification models often encounter noisy data deviating from training distributions.
Real-world applications frequently involve applying models to data from different sources or laboratories.

Purpose of the Study:

To evaluate the Diversified Multiple Tree (DMT) ensemble method's effectiveness in classifying data from a new laboratory.
To compare DMT's performance against established ensemble methods on cross-laboratory datasets.

Main Methods:

DMT was tested on three real-world biomedical datasets from distinct laboratories.
Comparative analysis included AdaBoost, Bagging, Random Forests, and Random Trees.
Investigated DMT's limitations and potential variations.

Main Results:

DMT demonstrated significantly higher accuracy in classifying instances from a new laboratory compared to training data.
Experimental results confirmed DMT's superior performance over benchmark ensemble classifiers.

Conclusions:

The Diversified Multiple Tree (DMT) ensemble classifier exhibits enhanced robustness for noisy data.
DMT proves more effective than other ensemble methods when classifying data with cross-laboratory variations.