Incremental learning and concept drift in INTHELEX

Abstract

Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called on-line) techniques are able to deal with such a situation by exploiting refinement operators. In many cases deep knowledge about the world is not available: Either incomplete information is available at the time of initial theory generation, or the nature of the concepts evolves dynamically. The latter situation is the most difficult to handle since time evolution needs to be considered. This work presents a new approach to learning in presence of concept drift, and in particular a special version of the incremental system INTHELEX purposely designed to implement such a technique. Its behavior in this context has been checked and analyzed by running it on two different datasets.