We then focus on improving the cost-efficiency of adaptation by selecting and prioritizing adaptation tasks involving multiple classifiers. We approach this problem by first conducting a comprehensive analysis of the generalizability of concept classifiers, which is related to the cost-efficiency of adapting a classifier. This analysis reveals strong correlations between generalizability and various meta-features of a classifier; ranging from model structure to the distribution of its output. We show that generalizability can be predicted quantitatively from these model meta-features using regression models. Based on the predictions of generalizability, we propose several selective adaptation methods for selecting the classifiers to be adapted and allocating their training examples such that they achieve higher overall post-adaptation performance than equally adapting every classifier.
