The Class Imbalance Problem: AdaBoost to the Rescue?
Channel: Alex Henderson
Date: 2021-08-26

Here we discuss the issues when applying Random Forests and AdaBoost data analysis methods to infrared spectroscopy data sets, where the numbers in each class vary. 
Invited presentation at the 11th International Conference on Advanced Vibrational Spectroscopy (ICAVS-11), 23-26 August 2021. This was a virtual conference.


This presentation relates to our paper in Analyst "Exploring AdaBoost and Random Forests machine learning approaches for infrared pathology on unbalanced data sets" by Jiayi Tang, Alex Henderson and Peter Gardner. 
Paper: https://doi.org/10.1039/D0AN02155E (available open access, CC-BY). 
Raw data: https://doi.org/10.5281/zenodo.4986399 (CC-BY) 
Processed data, and MATLAB source code: https://doi.org/10.5281/zenodo.4730312 (CC-BY)


Abstract
The use of infrared spectroscopy to augment decision-making in histopathology is a promising direction for the diagnosis of many disease types. Hyperspectral images of healthy and diseased tissue, generated by infrared spectroscopy, are used to build chemometric models that can provide objective metrics of disease state. It is important to build robust and stable models to provide confidence to the end user. The data used to develop such models can have a variety of characteristics which can pose problems to many model-building approaches. Here we have compared the performance of two machine learning algorithms – AdaBoost and Random Forests – on a variety of non-uniform data sets. Using samples of breast cancer tissue, we devised a range of training data capable of describing the problem space. Models were constructed from these training sets and their characteristics compared. In terms of separating infrared spectra of cancerous epithelium tissue from normal-associated tissue on the tissue microarray, both AdaBoost and Random Forests algorithms were shown to give excellent classification performance (over 95% accuracy) in this study. AdaBoost models were more robust when datasets with large imbalance were provided. The outcomes of this work are a measure of classification accuracy as a function of training data available, and a clear recommendation for choice of machine learning approach.