An Empirical Investigation into the Inconsistency of Sequential Active Learning

In active learning, one aims to acquire labeled samples that are particularly useful for training a classifier. In sequential active learning, this sample selection is done in a one-at-a-time manner where the choice of sample t + 1 may depend on the current state of the classifier and the t labeled data points already available. In their deviation from standard random sampling, current active learning schemes typically introduce severe sampling bias. Even though this fact has been acknowledged in the more theoretical contributions covering active learning, the more popular approaches largely ignore this bias. This work empirically investigates the consequences of their actions and sets out to identify the pros and cons of this way of dealing with the problem of active learning. Even though current techniques can provide excellent approaches to learning, we conclude that they provide inconsistent solutions and therefore, in a strict sense, do not solve the problem of active learning.