An ensemble based algorithm, Learn++.MT2, is introduced as an enhanced alternative to our previously reported incremental learning algorithm, Learn++. Both algorithms are capable of incrementally learning novel information from new datasets that consecutively become available, without requiring access to the previously seen data. In this contribution, we describe Learn++.MT2 which specifically targets incrementally learning from distinctly unbalanced data, where the amount of data that become available varies significantly from one database to the next. The problem of unbalanced data within the context of incremental learning is discussed first, followed by a description of the proposed solution. Initial, yet promising results indicate considerable improvement on the generalization performance and the stability of the algorithm.