A heterogeneous online learning ensemble for non-stationary environments

Mobin M. Idrees, Leandro L. Minku, Frederic Stahl, Atta Badii

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
289 Downloads (Pure)


Learning in non-stationary environments is a challenging task which requires the updating of predictive models to deal with changes in the underlying probability distribution of the problem, i.e., dealing with concept drift. Most work in this area is concerned with updating the learning system so that it can quickly recover from concept drift, while little work has been dedicated to investigating what type of predictive model is most suitable at any given time. This paper aims to investigate the benefits of online model selection for predictive modelling in non-stationary environments. A novel heterogeneous ensemble approach is proposed to intelligently switch between different types of base models in an ensemble to increase the predictive performance of online learning in non-stationary environments. This approach is Heterogeneous Dynamic Weighted Majority (HDWM). It makes use of “seed” learners of different types to maintain ensemble diversity, overcoming problems of existing dynamic ensembles that may undergo loss of diversity due to the exclusion of base learners. The algorithm has been evaluated on artificial and real-world data streams against existing well-known approaches such as a heterogeneous Weighted Majority Algorithm (WMA) and a homogeneous Dynamic Weighted Majority (DWM). The results show that HDWM performed significantly better than WMA in non-stationary environments. Also, when recurring concept drifts were present, the predictive performance of HDWM showed an improvement over DWM.
Original languageEnglish
Article number104983
Number of pages23
JournalKnowledge-Based Systems
Early online date23 Aug 2019
Publication statusE-pub ahead of print - 23 Aug 2019


  • Heterogeneous ensemble classifier
  • Majority Algorithm
  • Concept drift
  • Data stream mining


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