Abstract
Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal ‘start’ or ‘end’; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.
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Notes
Multi-step prediction implies that several predictions are made before the true values are known.
A caveat being semi-supervised learning under streaming data, Sect. 5.4.
See model building with embedded versus wrapper or filter frameworks for attribute selection [113].
See also ‘sample selection bias’, Sect. 2.1.
We note that this in itself is a function of assumptions made regarding parameterization. At some point decreasing the size of data chunks will result in performance approaching that of exemplar-wise updating. Observations of this type have informed the use of differing pairwise sliding window durations (Sect. 5.1.1) and evolved temporal features, e.g., [125, 184].
Introns, although non-coding for proteins in biology, appear to describe RNA that play an important role in gene regulation in eukaryotes [35]. In the case of GP, there is generally little or no distinction between genotype and phenotype, and more non-functional code observed than functional code [24, 115].
Younger/older individuals should only be maintained if they were suitably fit.
The majority of datasets employed to date for benchmarking purposes on account of their temporal properties are distributed across multiple repositories (Sect. 4.2.2).
In addition non-overlapping windows have been used, in particular with ensemble methods, with different members of the ensemble being constructed with each new ensemble location (see Sect. 3.2).
Denoting how much of a specific spatio-temporal basis function are present.
For example, as in parameterizing specific technical indicators for feature construction in finance [71].
The decision tree defines the condition under which an action is applied, say, as in sell, buy or hold.
Attempts to cast a multi-class classification problem into at least \(C - 1\) binary classification problems merely emphasizes this effect. Thus, even if the \(C\) classes appear with equal frequency, each binary classification task represents an unequal partition of one class versus the rest.
For a general discussion of this topic (albeit under a non-streaming scenario) see for example Chapter 9 from [59].
Potential short term research goals having been noted in the conclusion (Sect. 6).
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The author would like to thank the reviewers for their constructive feedback on this article resulting in significant improvements on earlier drafts. Support through the NSERC CRD Grant program and RUAG Schweiz AG is readily acknowledged.
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Heywood, M.I. Evolutionary model building under streaming data for classification tasks: opportunities and challenges. Genet Program Evolvable Mach 16, 283–326 (2015). https://doi.org/10.1007/s10710-014-9236-y
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DOI: https://doi.org/10.1007/s10710-014-9236-y