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Hybrid approaches to optimization and machine learning methods
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Orientador(es)
Resumo(s)
This paper conducts a comprehensive literature
review concerning hybrid techniques that combine optimization
and machine learning approaches for clustering and classification
problems. The aim is to identify the potential benefits of
integrating these methods to address challenges in both fields.
The paper outlines optimization and machine learning methods
and provides a quantitative overview of publications since 1970.
Additionally, it offers a detailed review of recent advancements
in the last three years. The study includes a SWOT analysis of
the top ten most cited algorithms from the collected database,
examining their strengths and weaknesses as well as uncovering
opportunities and threats explored through hybrid approaches.
Through this research, the study highlights significant findings
in the realm of hybrid methods for clustering and classification,
showcasing how such integrations can enhance the shortcomings
of individual techniques.
Descrição
Palavras-chave
Machine learning Optimization Hybrid methods Literature review Clustering Classification
Contexto Educativo
Citação
Azevedo, Beatriz Flamia; Rocha, Ana Maria A.C.; Pereira, Ana I. (2023). Hybrid approaches to optimization and machine learning methods. In 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA). p. 1-2. ISBN 979-8-3503-4503-2
Editora
IEEE