An Ensemble Approach for Incremental Learning in Nonstationary Environments

Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting – as used by most ensemble based nonstationary learning algorithms – a strategic weighting mechanism is employed that tracks the classifiers’ performances over drifting environments to determine appropriate voting weights. Specifically, the proposed approach generates a single classifier for each dataset that becomes available, and then combines them through a dynamically modified weighted majority voting, where the voting weights...

• Type: Conference
• Year: 2007
• Where: MCS
• Authors: Michael Muhlbaier, Robi Polikar
• Keywords: Appropriate Voting Weights, Classifiers Based Algorithm, MCS 2007, Pattern Recognition, Voting Weights