Ardabili, S.; Mosavi, A.; Várkonyi-Kóczy, A.R. Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods. Preprints2019, 2019080203. https://doi.org/10.20944/preprints201908.0203.v1
APA Style
Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A.R. (2019). Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>. Preprints. https://doi.org/10.20944/preprints201908.0203.v1
Chicago/Turabian Style
Ardabili, S., Amir Mosavi and Annamária R. Várkonyi-Kóczy. 2019 "Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>" Preprints. https://doi.org/10.20944/preprints201908.0203.v1
Abstract
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.
Keywords
machine learning; deep learning; ensemble models
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
