Concept Drift Adaptation by Exploiting Historical Knowledge

Yu SUN, Ke TANG, Zexuan ZHU, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

84 Citations (Scopus)

Abstract

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be retrained to attain new models for the current data. Two design questions need to be addressed in developing ensemble methods for incremental learning with concept drift, i.e., which historical (i.e., previously trained) models should be preserved and how to utilize them. A novel ensemble learning method, namely, Diversity and Transfer-based Ensemble Learning (DTEL), is proposed in this paper. Given newly arrived data, DTEL uses each preserved historical model as an initial model and further trains it with the new data via transfer learning. Furthermore, DTEL preserves a diverse set of historical models, rather than a set of historical models that are merely accurate in terms of classification accuracy. Empirical studies on 15 synthetic data streams and 5 real-world data streams (all with concept drifts) demonstrate that DTEL can handle concept drift more effectively than 4 other state-of-The-Art methods. © 2012 IEEE.
Original languageEnglish
Article number8246541
Pages (from-to)4822-4832
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number10
Early online date4 Jan 2018
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the Ministry of Science and Technology of China under Grant 2017YFC0804002, in part by the National Natural Science Foundation of China under Grant 61672478 and Grant 61329302, in part by the Science and Technology Innovation Committee Foundation of Shenzhen under Grant ZDSYS201703031748284, and in part by the Royal Society Newton Advanced Fellowship under Grant NA150123.

Keywords

  • Concept drift
  • data stream mining
  • ensemble learning
  • incremental learning
  • transfer learning

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