Multi-label classification via incremental clustering on an evolving data stream

Tien Thanh Nguyen, Manh Truong Dang, Anh Vu Luong, Alan Wee-Chung Liew, Tiancai Liang, John McCall

Abstract: Highlights•An incremental clustering-based multi-label online classification algorithm for multi-label data stream is proposed.•To handle concept drift, our algorithm evolves with time, giving higher attention to more recent samples than older samples through a weight decay mechanism.•Our algorithm dynamically determines the number of predicted labels based on Hoeffding inequality and the label cardinality.•Extensive comparative experiments with the state-of-the-art algorithms validated the superior performance of our algorithm in both the stationary and concept drift settings.

External IDs:doi:10.1016/j.patcog.2019.06.001