Application of Biologically Inspired Methods to Improve Adaptive Ensemble Learning

Grmanová, Gabriela; Rozinajová, Viera; Ezzedine, Anna Bou; Lucká, Mária; Lacko, Peter; Lóderer, Marek; Vrablecová, Petra; Laurinec, Peter

doi:10.1007/978-3-319-27400-3_21

Cited by 7 publications

(1 citation statement)

References 20 publications

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“…Furthermore, Seijo-Pardo et al (2017) employed both homogeneous and heterogeneous ensembles for feature selection. Zhao et al (2010) suggested that the heterogeneous bagging based ensemble strategy performs better than boosting based Learn++ algorithms and some other NCL methods.Other examples that employed homogeneous ensemble methods were used to deal with the presence of incremental tasks, such as concept drift(Minku et al, 2009), power load forecasting(Qiu et al, 2018;Grmanová et al, 2016), myoelectric prosthetic hands surface electromyogram characteristics(Duan and Dai, 2017), etc Das et al (2016). proposed an ensemble incremental learning with pseudo-outerproduct fuzzy neural network for traffic flow prediction, real-life stock price, and volatility predictions, etc.…”

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confidence: 99%

Ensemble deep learning: A review

Ganaie

Tanveer

et al. 2022

Engineering Applications of Artificial Intelligence

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mentioning

confidence: 99%

Ensemble deep learning: A review

Ganaie

Tanveer

et al. 2022

Engineering Applications of Artificial Intelligence

739

233

View full text Add to dashboard Cite

Intelligent Analysis of Data Streams

Rozinajová¹,

Ezzeddine²,

Grmanová³

et al. 2020

Towards Digital Intelligence Society

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Learning Under Concept Drift for Regression—A Systematic Literature Review

et al. 2022

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Context: The amount and diversity of data have increased drastically in recent years. However, in certain situations, the data to which a trained Machine Learning model is significantly different from testing data, a problem known as Concept Drift (CD). Because CD can be a serious issue, there has been a wealth of research on how to detect and work around it. However, most of the literature focuses on classification tasks. Objective: Making a Systematic Literature Review (SLR) for CD in the context of regression. Research questions: How to detect CD and how to build CD techniques for regression problems using machine learning? Method: We ran an automatic search process on reference databases, selecting papers from 2010 to August 2020, following the methodological process proposed by (Kitchenhame and Charters) (2007). Results:We selected 41 papers. Drift Detection Methods based on ensembles and neural networks with highlight OS-ELM were the most frequent in the selected papers with superior performance. However, only two papers confirm such superiority statistically. Furthermore, identify CD problems as the batch size, drift points, and where drift happens. Conclusions: SLR focuses on highlighting the existing literature on CD applied to regression.

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Application of Biologically Inspired Methods to Improve Adaptive Ensemble Learning

Cited by 7 publications

References 20 publications

Ensemble deep learning: A review

Ensemble deep learning: A review

Intelligent Analysis of Data Streams

Learning Under Concept Drift for Regression—A Systematic Literature Review

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