FLORA2 is a program for supervised learning of concepts that are subject to concept drift. The learning process is incremental in that the examples are processed one by one. A special feature of our program consists in keeping in memory a subset of examples -- a window. In time, new examples are being added to the window while other ones are considered outdated and are forgotten. In order to track the concept drift, the system keeps in memory not only valid descriptions of the concepts as they are derived from the objects currently present in the window, but also `candidate descriptions' that may turn into valid descriptions in the future. 1 Introduction One of the key tasks of the Machine Learning discipline is to find powerful methods for abstracting concepts out of a set of objects. Basically, two subproblems of this task exist: supervised learning and unsupervised learning. The former assumes that a set of preclassified examples (positive and negative) of some concept(s) are avail...