Large IT service providers track service requests and their execution through problem/change tickets. It is important to classify the tickets based on the problem/change description in order to understand service quality and to optimize service processes. However, two challenges exist in solving this classification problem: 1) ticket descriptions from different classes are of highly diverse characteristics, which invalidates most standard distance metrics; 2) it is very expensive to obtain high-quality labeled data. To address these challenges, we develop two seemingly independent methods 1) Discriminative Neighborhood Metric Learning (DNML) and 2) Active Learning with Median Selection (ALMS), both of which are, however, based on the same core technique: iterated representative selection. A case study on real IT service classification application is presented to demonstrate the effectiveness and efficiency of our proposed methods.