Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
This addresses the problem of high costs and ethical issues in obtaining labeled data for machine learning practitioners, but it is incremental as it reviews existing methods.
The paper reviews statistical semi-supervised learning approaches for classifier formation when training data includes limited labeled observations and many unlabeled ones, highlighting a recent result that classifiers from partially classified samples can achieve smaller expected error rates than those from fully classified samples.
There has been increasing attention to semi-supervised learning (SSL) approaches in machine learning to forming a classifier in situations where the training data for a classifier consists of a limited number of classified observations but a much larger number of unclassified observations. This is because the procurement of classified data can be quite costly due to high acquisition costs and subsequent financial, time, and ethical issues that can arise in attempts to provide the true class labels for the unclassified data that have been acquired. We provide here a review of statistical SSL approaches to this problem, focussing on the recent result that a classifier formed from a partially classified sample can actually have smaller expected error rate than that if the sample were completely classified.