An Active Learning Algorithm by Selecting the Most Possibly Wrong-Predicted Instances

Active learning methods can alleviate the efforts of labeling large amounts of instances by selecting and asking experts to label only the most informative examples. Sampling is a key factor influencing the performance of active learning. Currently, the leading methods of sampling generally choose the instance or instances that can reduce the version space by half. However, the strategy of halving the version space assumes each hypothesis in version space has equal probability to be the target function which can not be satisfied in real world problems. In this paper, the limitation of the strategy of halving the version space is analyzed. Then presented is a sampling method named MPWPS (the most possibly wrong-predicted sampling) aiming to reduce the version space more than half. While sampling, MPWPS chooses the instance or instances that would be most likely to be predicted wrong by the current classifier, so that more than half of hypotheses in the version space are eliminated. Comparing the proposed MPWPS method and the existing active learning methods, when the classifiers achieve the same accuracy, the former method will sample fewer times than the latter ones. The experiments show that the MPWPS method samples fewer instances than traditional sampling methods on most datasets when obtaining the same target accuracy.