Regression on imperfect class labels derived by unsupervised clustering
Machine Learning
2020-03-06 v1 Machine Learning
Methodology
Abstract
Outcome regressed on class labels identified by unsupervised clustering is custom in many applications. However, it is common to ignore the misclassification of class labels caused by the learning algorithm, which potentially leads to serious bias of the estimated effect parameters. Due to its generality we suggest to redress the situation by use of the simulation and extrapolation method. Performance is illustrated by simulated data from Gaussian mixture models. Finally, we apply our method to a study which regressed overall survival on class labels derived from unsupervised clustering of gene expression data from bone marrow samples of multiple myeloma patients.
Cite
@article{arxiv.1908.05885,
title = {Regression on imperfect class labels derived by unsupervised clustering},
author = {Rasmus Froberg Brøndum and Thomas Yssing Michaelsen and Martin Bøgsted},
journal= {arXiv preprint arXiv:1908.05885},
year = {2020}
}