An approach for online learning in the presence of concept changes. (Une approche pour l'apprentissage en-ligne en présence de changements de concept.)

An approach for online learning in the presence of concept change Learning from data streams is emerging as an important application area. When the environment changes, as is increasingly the case when considering unending streams and long-life learning, it is necessary to rely on on-line learning with the capability to adapt to changing conditions a.k.a. concept drifts. Adapting to concept drifts entails forgetting some or all of the old acquired knowledge when the concept changes while accumulating as much knowledge as possible when the underlying concept is supposed stationary. This tradeoff is called the stability-plasticity dilemma. Ensemble methods have been among the most successful approaches. However, the management of the ensemble which ultimately controls how past data is forgotten has not been thoroughly investigated so far. Our work shows the importance of the forgetting strategy by comparing several approaches. The results thus obtained lead us to propose a new ensemble method with an enhanced forgetting strategy to adapt to concept drifts. Experimental comparisons show that our method compares favorably with the wellknown state-of-the-art systems. The majority of previous works focused only on means to detect changes and to adapt to them. In our work, we go one step further by introducing a meta-learning mechanism that is able to detect relevant states of the environment, to recognize recurring contexts and to anticipate likely concepts changes. Hence, the method we suggest, deals with both the challenge of optimizing the stabilityplasticity dilemma and with the anticipation and recognition of incoming concepts. This is accomplished through an ensemble method that controls an ensemble of incremental learners. The management of the ensemble of learners enables one to naturally adapt to the dynamics of the concept changes with very few parameters to set, while a learning mechanism managing the changes in the ensemble provides means for the anticipation of, and the quick adaptation to, the underlying modification of the context. te l-0 09 07 48 6, v er si on 1 21 N ov 2 01 3