Ensemble learning for data stream analysis: A survey

Abstract

In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semisupervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.

Exploring complex and big data

“…The latter integrate several incremental single classifiers, and are often based on extending standard ensembles (like many variants of online bagging) or the weighted majority algorithm . Comprehensive reviews of various ensembles can be found in some recent surveys (Ditzler et al, 2015;Krawczyk et al, 2017). 6.3.…”

“…In recent years, class imbalances (also with variable class cardinalities or swapping roles of minority vs. majority classes) and detection of novel, appearing classes have been receiving increased interest (Sun et al, 2016). However, research on these topics is still in the early phase (Krawczyk et al, 2017).…”

Tensor-Based Shot Boundary Detection in Video Streams

“…Detection of different types of shots is a special case of the concept drift detection in streams of data [17,28,41]. In the case of video signals, analyzed in this paper, detection of different types of shots requires development of special methods which account for different statistical properties of the video signals.…”

“…Recently, these gained much attention among researchers. This new domain of data processing is analyzed, for instance, in the book by Gama [17] or in the papers by Krawczyk et al [28] or Woźniak et al [53], to name a few. The two domains-that is,streams of tensor data-were pioneered by the works by Sun et al [43,44], as will be further discussed.…”

“…This usually happens due to changes of the statistical properties of the data stream caused by external factors. The concept drift detection in different streams belongs to a dynamical research area [17,18,28]. In the case of video streams, a concept drift usually corresponds to content change in video signal due to a hard or soft shots, as already discussed.…”