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Showing 1 to 20 of 20 for “"LightGBM"”.
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Predicting smartphone email marketing campaign clicks with the LightGBM algorithm
… klikkausten mallintaminen ja ennus- taminen LightGBM algortimin avulla. Mainosten klikkaamisen ennustamista käytetään sähkö- posti markkinoinnin kohdentamiseen potentiaalisesti kiinnostuneille asiakkaille. Klikkaamisen en- nustamisessa käytetty aineisto haettiin DNA Oyj:n tietokannasta. …
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model
… Bidirectional Long Short-Term Memory, and LightGBM. Additionally, two hybrid machine learning methods are used for prediction: CNN-LSTM and BiLSTM-BO-LightGBM. After training the models, the algorithm creates an optimal portfolio of assets over a simulated year of trading. The symmetric …
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Cluster-enhanced Ensemble Learning for Mapping Surface Ozone in China
… neural networks (MLP), random forests (RF), LightGBM, XGBoost, and CatBoost, are applied to simulate ozone concentrations. Hyperparameters are optimized via randomized grid search to enhance model performance. To improve spatial prediction accuracy, geographically weighted generalized …
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Modelado de riesgo de usuarios receptores para la prevención de estafas en billeteras digitales
… (Regresión Logística (baseline), Random Forest y LightGBM) más un ensamble de los dos últimos, utilizando un split temporal estricto que replica el escenario real de producción. La métrica principal de comparación fue el Average Precision (AUC-PR), más informativa que el AUC-ROC en contextos de …
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Predicting mergers and acquisitions using machine learning
… performance of algorithms such as random forest, LightGBM, long short-term memory networks (LSTM) and the TabTransformer are evaluated against the baseline. A secondary objective is the development of a robust ensemble model for potential use in an investment portfolio. The algorithms were trained …
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Predicting outpatient appointment non-attendances using machine learning techniques
… medical specialties. A predictive model based on LightGBM was used to determine the risk-increasing and risk-mitigating factors for missing appointments, which were then used to assign a risk score to patients on an appointment-by-appointment basis for each specialty. Results show that the best …
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Predicting 30-Day Unplanned ICU Readmissions Using Deep Learning and Natural Language Processing Techniques: A MIMIC IV Data Analysis
… Dense Neural Networks (DNNs) combined with the LightGBM gradient-boosting framework. Our model attained a 5-fold cross-validated area under the ROC curve (AU- ROC) of 0.81. Our results demonstrate the effectiveness of the proposed modeling approach in identifying high-risk patients for unplanned …
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Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems
… environments. Using this framework, we benchmark LightGBM—a gradient boosting method known for its computational efficiency and predictive accuracy—on an imbalanced dataset, comparing its performance against standard academic evaluation criteria. Our results demonstrate that Tail-end FPR Max …
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A Comparative Study of Machine Learning Models for Multivariate NextG Network Traffic Prediction with SLA-based Loss Function
… two gradient-boosted tree models (XGBoost and LightGBM). The prediction performance of the models is evaluated based on different metrics such as SLA violation rate constraints, overprovisioning, and the custom SLA-based loss function parameter. According to our evaluations, Transformer models …
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An Interactive Learning Framework for Understanding Infrastructure Health Monitoring and Leveraging Machine Learning for Safety Improvement
… Networks (ANN), Light Gradient Boosting Machine (LightGBM), Random Forest (RF), K-Nearest Neighbors (KNN), and Ordinal Logistic Regression (OLR), the study quantifies the potential injury reduction benefits of infrastructure improvements. These data-driven predictions are validated against …
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Sensing and Predicting Urban Rail Platform Crowding Using Emerging Data Sources
… employ a gradient-boosted tree regression model (LightGBM) to leverage fare card transaction, vehicle location, weather, and public event data from the Washington Metropolitan Area Transit Authority (WMATA) to forecast platform-level occupancies 15–60 minutes ahead of time. Our results show …
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Machine Learning Applications in Blockchain for Renewable Energy Systems
… (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest is conducted for residual demand. To optimize these models, the study contrasts bio-inspired Swarm Intelligence, Honey Badger Algorithm (HBA), Particle Swarm Optimization (PSO) with probabilistic Gaussian Process …
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Optimizing Deep Learning and Machine Learning Models for Real-Time Intrusion Detection in IoT Networks
… higher training time (2835.6 s), while LightGBM offered a favorable efficiency profile (Macro-F1=0.9360, 40.6 s). Deep learning baselines underperformed leading ensembles (best DL: Wide MLP Macro-F1=0.7666) and one attention-based model collapsed (MacroF1=0.0031), indicating sensitivity …
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Toward explainable machine learning methods for stroke patient outcomes in Tennessee
… suitable for imbalanced data, such as XGBoost, LightGBM, and CatBoost, were employed in this work. To further improve the performance of the models, various data-level approaches were used to overcome the imbalanced nature of the data. These methods include cluster centroids, NearMiss, and …
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Machine Learning Methods for Wastewater Treatment Plants
… particularly gradient boosting methods such as LightGBM. This model was implemented in real plants as a Decision Support System that can alert plant operators, and subsequently integrated into a new aeration controller that automatically reacts to events without the need of operator …
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InSAR time series analysis and machine learning for ground subsidence monitoring and susceptibility mapping in Midvaal, South Africa
… algorithms including Random Forest, XGBoost, LightGBM, and CNN were employed to create ground subsidence susceptibility maps and classify the region into five risk zones: Very Low, Low, Moderate, High, and Very High. Random Forest (RF) achieved the highest predictive accuracy with a Root Mean …
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Enhancement of digital elevation models using tree-based ensemble machine learning algorithms
… boosting (XGBoost), light boosting machine (LightGBM) and categorical boosting (CatBoost) were selected for the development of a robust DEM enhancement framework. After training and testing, the models were applied for correcting the DEMs at two implementation sites spread across the five …
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Predictive Modeling and Durability Analysis of Low Carbon Concrete Incorporating Recycled Materials
… Among them, the Light Gradient Boosting Machine (LightGBM) provided optimal predictive performance (R² = 0.94), and the most critical variables were related to age, water-to-cement ratio, and fine RCA content. The study also revealed that partially saturated RCA provided optimal strength results, …
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Machine Learning Approaches for Improving Construction Materials and Pavement Systems
… SMOTE and Light Gradient Boosting Machine (LightGBM) K-fold model, the present work could effectively predict compressive strength of concrete with SFS aggregates. The model showed that concrete with SFS aggregates can have statistically significant increases in compressive strength over …