Apprentissage ensembliste | EPFL Graph Search

En statistique et en apprentissage automatique, l'apprentissage ensembliste utilise plusieurs algorithmes d'apprentissage pour obtenir de meilleures prédictions. Par exemple, l'ensemble de méthodes bagging, boosting et les forêts aléatoires est un exemple d'apprentissage ensembliste. Un ensemble combine k méthodes, M_1 , M_2 , ..., M_k . Notes et références Catégorie:Apprentissage automatique

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Yuqing Liu

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (ATDF) to precisely predict the stud shear stiffness, where the novel Deep For-est algorithm is integrated with the Sequential Model-Based Optimization method to achieve automatic hyper -parameter optimization. Six variables having causal relationships with shear stiffness were extracted via mechanism and model analysis, including the effect of weld collar that cannot be considered in existing models and subsequently constituting a database of 425 push-out tests. Then the ATDF model was trained by combining the advantages of deep learning, ensemble learning, and auto-tuning techniques. It was approved to significantly outperform representative benchmark models with R values of 0.91 and 0.87 for training and testing sets. The ATDF was subjected to attribute importance analysis, which quantified the stud diameter and concrete elastic modulus as the most significant variables for shear stiffness, with the stud elastic modulus having the minimal effect. The model uncertainty of ATDF was further evaluated, revealing that it had the lowest bias and variability than those in existing empirical or semi-empirical models. Finally, the reliability analysis was conducted and the partial factors of ATDF under specified target reliability were derived.

2022

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri's movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 +/- 1.36% and 92.25 +/- 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 +/- 2.02, 98.32 +/- 1.93, 98.32 +/- 1.93, and 98.88 +/- 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 +/- 1.73 and 3.44 +/- 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 +/- 0.08 and 97.85 +/- 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control.

FRONTIERS MEDIA SA2022

Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Yuqing Liu

The shear resistance of headed studs is of paramount importance for the design of steel-concrete composite structures and an accurate predictive model is highly needed. Ensemble learning is expected to be a powerful solution while it relies on laborious selection of suitable hyper parameters. For efficiently predicting the resistance of headed studs, this work presented an auto-tuning ensemble learning-based strategy. It employed Sequential Model-Based Optimization method with Gaussian processes (GP) or Probabilistic random forests (PRF) as surrogate model to automatically explore the hyper-parameter configurations of ensemble learning algorithm-Light gradient boosting machine (LightGBM). To this end, the largest stud database to date of 1092 tests was established. The shear mechanisms of studs were analyzed and then integrated into the models via feature extraction and combination. The performance of GP-LightGBM and PRFLightGBM were assessed to outperform LightGBM and three standalone models, with PRFLightGBM being the most accurate. The superiority of PRF-LightGBM was further confirmed by comparison with the code equations in EC 4, AASHTO, GB 50017, and JSCE. Data-driven interpretation on PRF-LightGBM quantitatively revealed that the tensile capacity of stud shank and concrete performance are the most influential features on the shear resistance followed by the projected area of weld collar and longitudinal spacing of studs. Finally, an application StudATEML was created for efficient evaluation and practical design of headed stud connection.

2022

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

FRONTIERS MEDIA SA2022