A Review on Ensemble Methods and their Applications to Optimization Problems

Camacho-Gómez, Carlos; Salcedo‐Sanz, Sancho; Camacho, David

doi:10.1007/978-981-16-0662-5_2

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Machine Learning Models For Peptide–protein Interaction Pred...1

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Cited by 4 publications

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“… 31 Ensemble learning stands out as an effective strategy for handling imbalanced datasets, as it allows multiple models to collectively contribute to predictions, resulting in enhanced robustness, reduced variance, and improved generalization. 72 …”

Section: Machine Learning Models For Peptide–protein Interaction Pred...mentioning

confidence: 99%

Leveraging machine learning models for peptide–protein interaction prediction

Yin,

Mi,

Shukla

2024

RSC Chem. Biol.

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Section: Machine Learning Models For Peptide–protein Interaction Pred...mentioning

confidence: 99%

Leveraging machine learning models for peptide–protein interaction prediction

Yin,

Mi,

Shukla

2024

RSC Chem. Biol.

View full text Add to dashboard Cite

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Metaheuristic-based ensemble learning: an extensive review of methods and applications

Rezk,

Selim

2024

Neural Comput & Applic

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Ensemble learning has become a cornerstone in various classification and regression tasks, leveraging its robust learning capacity across disciplines. However, the computational time and memory constraints associated with almost all-learners-based ensembles necessitate efficient approaches. Ensemble pruning, a crucial step, involves selecting a subset of base learners to address these limitations. This study underscores the significance of optimization-based methods in ensemble pruning, with a specific focus on metaheuristics as high-level problem-solving techniques. It reviews the intersection of ensemble learning and metaheuristics, specifically in the context of selective ensembles, marking a unique contribution in this direction of research. Through categorizing metaheuristic-based selective ensembles, identifying their frequently used algorithms and software programs, and highlighting their uses across diverse application domains, this research serves as a comprehensive resource for researchers and offers insights into recent developments and applications. Also, by addressing pivotal research gaps, the study identifies exploring selective ensemble techniques for cluster analysis, investigating cutting-edge metaheuristics and hybrid multi-class models, and optimizing ensemble size as well as hyper-parameters within metaheuristic iterations as prospective research directions. These directions offer a robust roadmap for advancing the understanding and application of metaheuristic-based selective ensembles.

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