Double-Coupling Learning for Multi-Task Data Stream Classification
Abstract
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handling them separately, it is beneficial to consider the correlations among the multi-task data streams for data stream modeling tasks. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM), is proposed for classifying them simultaneously. DC-SVM considers the external correlations between multiple data streams, while handling the internal relationship within the individual data stream. Experimental results on artificial and real-world multi-task data streams demonstrate that the proposed method outperforms traditional data stream classification methods.
Cite
@article{arxiv.1908.06021,
title = {Double-Coupling Learning for Multi-Task Data Stream Classification},
author = {Yingzhong Shi and Zhaohong Deng and Haoran Chen and Kup-Sze Choi and Shitong Wang},
journal= {arXiv preprint arXiv:1908.06021},
year = {2019}
}
Comments
This work has been accepted conditionally by IEEE Computational Intelligence Magazine in July 2019