{"total":18,"rows":[{"id":"2304.02858","title":"A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation","created":"2023-04-06","cited_by_count":25,"area":null},{"id":"2305.09536","title":"A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them","created":"2023-05-16","cited_by_count":15,"area":null},{"id":"2301.01252","title":"Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data","created":"2022-12-17","cited_by_count":6,"area":null},{"id":"2301.01214","title":"Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale","created":"2022-12-31","cited_by_count":4,"area":null},{"id":"2207.03317","title":"Multimodal Feature Extraction for Memes Sentiment Classification","created":"2022-07-07","cited_by_count":3,"area":null},{"id":"2307.06840","title":"Ensemble learning for blending gridded satellite and gauge-measured precipitation data","created":"2023-07-09","cited_by_count":3,"area":null},{"id":"2311.12604","title":"Trustworthy AI: Deciding What to Decide","created":"2023-11-21","cited_by_count":3,"area":null},{"id":"2403.10567","title":"Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products","created":"2024-03-14","cited_by_count":3,"area":null},{"id":"2109.02624","title":"Functional additive models on manifolds of planar shapes and forms","created":"2021-09-06","cited_by_count":2,"area":null},{"id":"2303.07139","title":"Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study","created":"2023-03-13","cited_by_count":2,"area":null},{"id":"2303.08625","title":"Interpretable Ensembles of Hyper-Rectangles as Base Models","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2308.02588","title":"Unmasking Parkinson's Disease with Smile: An AI-enabled Screening Framework","created":"2023-08-03","cited_by_count":2,"area":null},{"id":"2106.05799","title":"Hybrid Machine Learning Forecasts for the UEFA EURO 2020","created":"2021-06-07","cited_by_count":1,"area":null},{"id":"2210.04869","title":"A copula-based boosting model for time-to-event prediction with dependent censoring","created":"2022-10-10","cited_by_count":1,"area":null},{"id":"2104.07985","title":"Probabilistic water demand forecasting using quantile regression algorithms","created":"2021-04-16","cited_by_count":0,"area":null},{"id":"2403.13672","title":"Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations","created":"2024-03-20","cited_by_count":0,"area":null},{"id":"2406.17308","title":"Improving Realized LGD Approximation: A Novel Framework with XGBoost for Handling Missing Cash-Flow Data","created":"2024-06-25","cited_by_count":0,"area":null},{"id":"2407.01623","title":"Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products","created":"2024-06-29","cited_by_count":0,"area":null}],"next_cursor":null}