Heterogeneous ensemble selection for evolving data streams

Anh Vu Luong, Tien Thanh Nguyen, Alan Wee-Chung Liew, Shilin Wang

Abstract: Highlights•An online ensemble selection method that takes into account each heterogeneous base classifier's confidence during classification and its overall accuracy on the data stream is proposed.•Each base classifier's confidence in classification for a test sample is estimated by a threshold computed dynamically using stochastic gradient descent.•The overall accuracy of the base classifier is computed using the prequential accuracy that emphasizes more recent instances in the data stream.•Extensive comparative experiments with the state-of-the-art algorithms on online ensemble selection validated the superior performance of our algorithm.

External IDs:doi:10.1016/j.patcog.2020.107743