We have recently introduced an incremental learning algorithm, called Learn ++ .NSE, designed for Non-Stationary Environments (concept drift), where the underlying data distribution changes over time. With each dataset drawn from a new environment, Learn ++ .NSE generates a new classifier to form an ensemble of classifiers. The ensemble members are combined through a dynamically weighted majority voting, where voting weights are determined based on classifiers' age-adjusted accuracy on current and past environments. Unlike other ensemble-based concept drift algorithms, Learn ++ .NSE does not discard prior classifiers, allowing potentially cyclical environments to be learned more effectively. While Learn ++ .NSE has been shown to work well on a variety of concept drift problems, a potential shortcoming of this approach is the cumulative nature of the ensemble size. In this contribution, we expand our analysis of the algorithm to include various ensemble pruning methods to introduce controlled forgetting. Error or age-based pruning methods have been integrated into the algorithm to prevent potential out-voting from irrelevant classifiers or simply to save memory over an extended period of time. Here, we analyze the tradeoff between these precautions and the desire to handle recurring contexts (cyclical data). Comparisons are made using several scenarios that introduce various types of drift.
['Ryan Elwell', 'Robi Polikar']
Incremental learning in nonstationary environments with controlled forgetting