CIS Publication Spotlight [Publication Spotlight]
Digital Object Identifier: 10.1109/ TNN.2011.2160459 “An ensemble of classifiers-based approach is introduced for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that exper ience constant or variable rate of drift, addition or deletion of concept classes, as well as cyclical drift. The algorithm learns incremental ly without requiring access to previously seen data. The novelty of the approach is in determining the voting weights, based on each classifier’s time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as well as to a possible reoccurrence of an earlier distribution. The algorithm is evaluated on several synthetic datasets designed to simulate a variety of nonstationary environments, as well as a real-world weather prediction dataset.”