Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model; the Gradient Boosting Decision Tree (...
3. Machining Quality Prediction Method Based on XGBoost Hyperparameter Optimization Establishing a processing quality prediction model for key quality characteristics allows for evaluating processing quality before production, thereby reducing economic losses caused by substandard processing quality. This study ut...
would be utilized as input features for the proposed hybrid stacking model based on different Artificial Intelligence (AI) methods. Different from the conventional load forecasting method, the proposed hybrid stacking method includes multiple AI models including XGBoost, CatBoost, Neural Oblivious Decision...
Conference paper KubeVirt scale test by creating 400 VMIs on a single node Paper Challenges and experiences in building an efficient apache beam runner for IBM streams
We conducted comparative experiments using Support Vector Machines (SVM), Long Short-Term Memory (LMST), One-Dimensional Convolutional Neural Networks (1D-CNN) and Extreme Gradient Boosting (XGBoost) respectively. To make the results more convincing, we made the structure of the 1D-CNN consistent ...
XGBoost: A Scalable Tree Boosting System (KDD 2016) Tianqi Chen, Carlos Guestrin [Paper] [Code] Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale (NIPS 2016) Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S. Talwalk...
Quant Model Zoo Here is a list of models built onQlib. GBDT based on XGBoost (Tianqi Chen, et al. 2016) GBDT based on LightGBM (Guolin Ke, et al. 2017) GBDT based on Catboost (Liudmila Prokhorenkova, et al. 2017) MLP based on pytorch ...
XGBoost 2 X X ZeroR 2 X Ant Colony Optimization 1 X X X Bayesian Inference 1 X Convolutional Neural Network 1 X Fuzzy Support System 1 X X X Gaussian Process 1 X X Gaussian Distribution Model 1 X Kohonen Network 1 X Linear Dirichlet Allocation 1 X Multiple Linear Regression 1...
In this paper, the improved machine learning algorithm XGBoost and the finite element numerical simulation method are used to establish the blasting vibration velocity prediction model of the Dahuangshan tuff rock mine. This paper focuses on the prediction of blasting vibration velocity....
Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict b