Ensemble Learning Through Evolutionary Multitasking: A Formulation and Case Study
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- @Article{Liaw:ETCI,
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author = "Rung-Tzuo Liaw and Yu-Wei Wen",
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journal = "IEEE Transactions on Emerging Topics in Computational
Intelligence",
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title = "Ensemble Learning Through Evolutionary Multitasking: A
Formulation and Case Study",
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note = "Early access",
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abstract = "Evolutionary machine learning has drawn much
attentions on solving data-driven learning problem in
the past decades, where classification is a major
branch of data-driven learning problem. To improve the
quality of obtained classifier, ensemble is a simple
yet powerful strategy. However, gathering classifiers
for ensemble requires multiple runs of learning process
which bring additional cost at evaluation on the data.
This study proposes an innovative framework for
ensemble learning through evolutionary multitasking,
i.e., the evolutionary multitasking for ensemble
learning (EMTEL). There are four main features in the
EMTEL. First, the EMTEL formulates a classification
problem as a dynamic multitask optimisation problem.
Second, the EMTEL uses evolutionary multitasking to
resolve the dynamic multitask optimisation problem for
better convergence through the synergy of common
properties hidden in the tasks. Third, the EMTEL
incorporates evolutionary instance selection for saving
the cost at evaluation. Finally, the EMTEL formulates
the ensemble learning problem as a numerical
optimisation problem and proposes an online ensemble
aggregation approach to simultaneously select
appropriate ensemble candidates from learning history
and optimise ensemble weights for aggregating
predictions. A case study is investigated by
integrating two state-of-the-art methods for
evolutionary multitasking and evolutionary instance
selection respectively, i.e., the symbiosis in
biocoenosis optimisation and cooperative evolutionary
learning and instance selection. For online ensemble
aggregation, this study adopts the well-known
covariance matrix adaptation evolution strategy.
Experiments validate the effectiveness of the EMTEL
over conventional and advanced evolutionary machine
learning algorithms, including genetic programming,
self-learning gene expression programming, and
multi-dimensional genetic programming. Experimental
results show that the proposed framework ameliorates
state-of-the-art methods, and the improvements on
quality for multiclass classification are at
8.48percent at least and 56.35percent at most in
relation to the macro F-score. For convergence speed,
the speedups achieved by the proposed framework are
7.85 at least and 100.53 at most on multiclass
classification.",
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keywords = "genetic algorithms, genetic programming, Task
analysis, Optimisation, Symbiosis, Multitasking,
Statistics, Sociology, Knowledge transfer, Evolutionary
multitasking, dynamic multitask optimisation, online
ensemble learning, evolutionary machine learning,
evolutionary instance selection",
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DOI = "doi:10.1109/TETCI.2024.3369949",
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ISSN = "2471-285X",
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notes = "Also known as \cite{10463524}",
- }
Genetic Programming entries for
Rung-Tzuo Liaw
Yu-Wei Wen
Citations