Preprint Review Version 1 This version is not peer-reviewed

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Version 1 : Received: 16 August 2019 / Approved: 20 August 2019 / Online: 20 August 2019 (08:41:28 CEST)

How to cite: Ardabili, S.; Mosavi, A.; Várkonyi-Kóczy, A.R. Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods . Preprints 2019, 2019080203 (doi: 10.20944/preprints201908.0203.v1). Ardabili, S.; Mosavi, A.; Várkonyi-Kóczy, A.R. Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods . Preprints 2019, 2019080203 (doi: 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.

Subject Areas

machine learning; deep learning; ensemble models

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.