Ensemble machine learning on gene expression data for cancer classification.

By Aik Choon Tan, David R. Gilbert. Applied Bioinformatics, 2003

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Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “This technique is also called ensemble learning, which has been a very popular learning method for decades in the machine learning community (Tan and Gilbert, 2003).”
Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, background): “In this work, ensemble ML (bagged and grown decision trees) outperformed single decision trees [107].”
A New Multi-Task Learning Technique to Predict Classification of Leukemia and Prostate Cancer (2010, methodology): “There are several machine learning techniques such as k-nearest neighbours, clustering methods, self organizing maps (SOM) [6], back-propagation neural network [7, 8] , probabilistic neural network, decision tree [9], random forest [10], support vector machines (SVM) [11, 12], multicategory support ”
A Review of Ensemble Methods in Bioinformatics (2010, unknown): “In comparison to the single tree classifier, Tan and Gilbert [34] demonstrated that, overall, ensemble methods of bagging and boosting are more robust and accurate in microarray data classification using seven publicly available datasets.”