Nested ensemble selection: An effective hybrid feature selection method

Kamalov, Firuz; Sulieman, Hana; Moussa, Sherif; Reyes, Jorge Avante; Safaraliev, Murodbek

doi:10.1016/j.heliyon.2023.e19686

Cited by 3 publications

(1 citation statement)

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“…With the advent of computational power and data-driven techniques, machine learning has emerged as a promising tool in many applications [4,5,6,7] including for predicting concrete compression strength. Machine learning models are able to learn complex non-linear relationships between the features (like mix proportions, age, and type of additives) and the target variable (compressive strength) from historical data.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of machine learning models for predicting concrete compressive strength

Saidjon Kamolov

2024

AMCS

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Predicting the compressive strength of concrete provides several benefits including insights into its durability, potential lifespan, and its suitability for specific construction projects. Recent advances in machine learning algorithms have prompted their application to compressive strength modeling and prediction. In this paper, we carry out a comprehensive comparative analysis of the existing machine learning algorithms. Unlike, other current studies in this domain, we consider the entire spectrum of algorithms ranging from basic linear models to advanced meta-learning methods. The results show that there exists a trade-off between speed and accuracy of the models. We find that while ensemble models such as stacking and gradient boost provide superior accuracy, they are significantly slower than linear models.

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Section: Introductionmentioning

confidence: 99%