TAIGA: a novel dataset for multitask learning of continuous and categorical forest variables from hyperspectral imagery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3141217
Abstract: The spectral and spatial resolutions of modern optical Earth observation data are continuously increasing. To fully utilize the data, integrate them with other information sources and create applications relevant to real-world problems, extensive training data… read more here.
Keywords: taiga; hyperspectral imagery; dataset; continuous categorical ... See more keywords
Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2024.3519537
Abstract: Surrogate-assisted evolutionary algorithms for addressing expensive optimization problems with both continuous and categorical variables (EOPCCVs) are still in the early stages of development. This study makes significant advancements by leveraging the mixed-variable nature of EOPCCVs… read more here.
Keywords: categorical variables; customized evolutionary; continuous categorical; optimization ... See more keywords
Standardization of continuous and categorical covariates in sparse penalized regressions
Sign Up to like & getrecommendations! Published in 2022 at "Statistical Methods in Medical Research"
DOI: 10.1177/09622802221129042
Abstract: In sparse penalized regressions, candidate covariates of different units need to be standardized beforehand so that the coefficient sizes are directly comparable and reflect their relative impacts, which leads to fairer variable selection. However, when… read more here.
Keywords: covariates sparse; standardization continuous; sparse penalized; categorical covariates ... See more keywords
Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India
Sign Up to like & getrecommendations! Published in 2024 at "Agronomy"
DOI: 10.3390/agronomy14112707
Abstract: Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the… read more here.
Keywords: deep learning; using deep; soil; continuous categorical ... See more keywords