Training the SOFM efficiently: an example from intrusion detection

The dynamic subset selection (DSS) active learning algorithm is generalized to include the case of unsupervised learning. To do so, training set partitioning, exemplar difficulty and age, and early stopping criteria are introduced into the self organizing feature map algorithm. The resulting model is able to build a hierarchical SOFM on a large (500,000 pattern) dataset in 3 hours. In comparison, the same architecture without active learning requires 33 hours to construct. No reduction in accuracy is recorded for the DSS SOFM model.