2) Importance-Weighting (IW): Importance weighting is a common technique in active learning literature (Tong and Koller, 2002;Beygelzimer et al., 2009;Beygelzimer et al., 2010), which mitigates the problem that if we query questions actively instead of selecting them uniformly at random, the training (and test) question sets are no longer independent and identically distributed (i.i.d.).
the training set will have a sample selection bias that can impair prediction performance.