In many application areas where databases are mined for classification rules, the latter may be subject to concept drift, that is change over time. Mining without taking this into account can result in severe degradation of the acquired classifier’s performance. This is especially the case when mining is conducted incrementally to maintain knowledge used by an on-line system. The TSAR methodology detects and copes with drift in such situations through the use of a time stamp attribute, applied to incoming batches of data, as an integral part of the mining process. Here we extend the use of TSAR by employing more refined time stamps: first to individual batches, then to individual examples within a batch. We develop two new decision tree based TSAR algorithms, CD4 and CD5 and compare these to our original TSAR algorithm CD3.