Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy

Research output: Chapter

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages215-227
Number of pages13
DOIs
Publication statusPublished - jan. 1 2020

Publication series

NameLecture Notes in Networks and Systems
Volume101
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Ardabili, S., Mosavi, A., & Várkonyi-Kóczy, A. R. (2020). Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods. In Lecture Notes in Networks and Systems (pp. 215-227). (Lecture Notes in Networks and Systems; Vol. 101). Springer. https://doi.org/10.1007/978-3-030-36841-8_21