The adequate operation of IPTV distribution networks heavily relies on the effective maintenance and management of their underlay DSL infrastructure. New hardware and software is required in order to improve monitoring capabilities and to directly diagnose anomalies that other segments of the DSL network cannot identify. In this work we initially compare the accuracy performance of SVM-specific formulations for constructing a robust ground truth within our classification procedure regarding abnormalities issued at anomaly-aware Digital Subscriber Line Access Multiplexers (DSLAMs) of the DSL infrastructure. Moreover, we consider the pragmatic cost of repairing anomalies that were misclassified and characterize each classifier according to the overall cost that is possible to incur to the network operator. In parallel, this work attempts to practically improve the network-wide anomaly classification performance by proposing a semi-supervised classification scheme that updates the initial supervised scheme by testing unlabelled anomalies occurring at anomaly-unaware DSLAMs.