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AdaBoost and Random Forests machine learning methods are compared using infrared hyperspectral images of breast cancer tissue with unbalanced class sizes. AdaBoost outperforms Random Forests for small spectral numbers and large class imbalance.

Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets