Multistream regression with asynchronous concept drift detection

Bo Dong, Yifan Li, Yang Gao, Ahsanul Haque, Latifur Khan, Mohammad M. Masud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

A recently introduced problem setting, referred as multistream, involves two independent non-stationary data generating processes. One of them is called source stream, which generates continuous data instances with true output. And the other one called target stream, which generates data instances lacking of true output. Due to the nature of data streams, scholars have addressed prediction problems under scenarios such as covariate shift or concept drift in past studies by discussing one assumption while keeping others consistent. For example, it is assumed that the data distributions of training and testing data are similar, and true output values of the stream instances would be available soon. However, in practice these assumptions are not always valid. The multistream regression problem is to predict the output of target stream, using data instances and their true output from source stream. In this paper, we propose an approach of multistream regression by incorporating concept drift detection into covariate shift adaptation. Meanwhile, empirical evaluation on synthetic and real world datasets demonstrates the effectiveness of the proposed technique by competing with the state-of-the-art approaches. Experiment results indicate that our method significantly improved prediction performance compared to existing benchmark.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages596-605
Number of pages10
ISBN (Electronic)9781538627143
DOIs
Publication statusPublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
Country/TerritoryUnited States
CityBoston
Period12/11/1712/14/17

Keywords

  • concept drift
  • covariate shift
  • multistream
  • regression

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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