首页|Reports Outline Machine Learning Study Results from Anna University (An Optimiza tion-based Stacked Ensemble Regression Approach To Predict the Compressive Stren gth of Self-compacting Concrete)
Reports Outline Machine Learning Study Results from Anna University (An Optimiza tion-based Stacked Ensemble Regression Approach To Predict the Compressive Stren gth of Self-compacting Concrete)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating in Tiruchirappalli, India, by NewsRx journalists, research stated, “This research paper presents a study on p redicting the compressive strength of self-compacting concrete (SCC) containing glass aggregate. A stacked ensemble approach was employed, which is a method of combining multiple models to improve the overall performance.” The news reporters obtained a quote from the research from Anna University, “The ensemble consisted of gradient boosting, extreme gradient boost, random forest, and K-nearest neighbors regressors as base learners, and linear regression as t he glass aggregates (FGA), coarse aggregates, coarse glass aggregates (CGA), and superplasticizer were taken as input variables and compressive strength as outp ut variables. The hyperparameters of the base learners were optimized using tree based pipeline optimization (TPOT). The ensemble’s accuracy was evaluated using the K-fold cross-validation technique and statistical metrics. The performance of the stacked ensemble models is found to be better than other machine learning models. Permutation feature importance was used to determine the importance of the features in predicting compressive strength. The results demonstrate that th e stacked ensemble approach with R2 = 0.9866, RMSE = 1.4730, and MAE = 1.0692 pe rformed better than the individual base learners and the other machine learning models.”
TiruchirappalliIndiaAsiaCyborgsE merging TechnologiesMachine LearningAnna University
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