Query-by-committee improvement with diversity and density in batch active learning

Active learning has gained attention as a method to expedite the learning curve of classifiers. To this end, uncertainty sampling is a widely adopted strategy that selects instances closer to the decision boundary. However, uncertainty sampling alone may not be sufficient in batch active learning due to the redundancy of instances and its susceptibility to outliers. In this study, we utilize query-by-committee (QBC) for uncertainty and demonstrate that its performance can be improved by introducing diversity and density in instance utility. Test results show that uncertainty sampling by QBC can be significantly improved with diversity and density incorporated in instance selection. Furthermore, we investigate several distance measures for use in diversity and density and show that random forest dissimilarity can be an effective distance measure in batch active learning. The effects of the characteristics of the data on the results are also analyzed.