An empirical study of cost-sensitive learning in cultural modeling

Cultural modeling aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods and in particular classification, play a central role in such applications. In modeling cultural data, it is expected that standard classifiers yield good performance under the assumption that different classification errors have uniform costs. However, this assumption is often violated in practice. Therefore, the performance of standard classifiers is severely hindered. To handle this problem, this paper empirically studies cost-sensitive learning in cultural modeling. We consider cost factor when building the classifiers, with the aim of minimizing total misclassification costs. We conduct experiments to investigate four typical cost-sensitive learning methods, combine them with six standard classifiers and evaluate their performance under various conditions. Our empirical study verifies the effectiveness of cost-sensitive learning in cultural modeling. Based on the experimental results, we gain a thorough insight into the problem of non-uniform misclassification costs, as well as the selection of cost-sensitive methods, base classifiers and method-classifier pairs for this domain. Furthermore, we propose an improved algorithm which outperforms the best method-classifier pair using the benchmark cultural datasets.