Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

  • Sina Ardabili
  • Amir MosaviEmail author
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)


The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.


Machine learning Deep learning Ensemble models 



Artificial neural network


Extreme learning machine


Machine learning


Support vector machine


Wavelet neural networks


Deep learning


Autoregressive integrated moving average


Ensemble empirical with adaptive noise technology


Data assimilation Kalman filter-based


Online sequential extreme learning machine


Bagging-based naïve bayes trees


Ensemble empirical mode decomposition


Grasshopper optimization algorithm


Hybrid of linear regression-deep neural network


Trauma Severity model


Gradient boosting decision tree


Evidential belief function and tree-based models


Decision tree overfitting and neural network


Improved complete ensemble empirical mode decomposition method with adaptive noise


Random forest



This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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