An empirical evaluation of algorithms for data labeling

Paper in proceeding, 2021

The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is a lack of research that investigates how these algorithms compare to each other for different types of datasets and with different percentages of available labels. To address this problem, this paper empirically evaluates and compares seven algorithms for automated labeling in terms of their accuracy. We investigate how these algorithms perform in twelve different and well-known datasets with three different types of data, images, texts, and numerical values. We evaluate these algorithms under two different experimental conditions, with 10% and 50% labels of available labels in the dataset. Each algorithm, in each dataset for each experimental condition, is evaluated independently ten times with different random seeds. The results are analyzed and the algorithms are compared utilizing a Bayesian Bradley-Terry model. The results indicate that the active learning algorithms using the query strategies uncertainty sampling, QBC and random sampling are always the best algorithms. However, this comes with the expense of increased manual labeling effort. These results help machine learning practitioners in choosing optimal machine learning algorithms to label their data.