Search alternatives:
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
input feature » input features (Expand Search)
binary input » binary depot (Expand Search)
primary data » primary care (Expand Search)
data model » data models (Expand Search)
feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
input feature » input features (Expand Search)
binary input » binary depot (Expand Search)
primary data » primary care (Expand Search)
data model » data models (Expand Search)
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Data Sheet 1_TBESO-BP: an improved regression model for predicting subclinical mastitis.pdf
Published 2025“…The model is based on TBESO (Multi-strategy Boosted Snake Optimizer) and utilizes monthly Dairy Herd Improvement (DHI) data to forecast the status of subclinical mastitis in cows.…”
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Data used in this study.
Published 2024“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”
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<i>hi</i>PRS algorithm process flow.
Published 2023“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”
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Machine learning deployment strategies and schematic illustration of the proposed generative adversarial algorithm for domain adaptation.
Published 2022“…<p><b>(A)</b> There are four primary methods by which machine learning models can be deployed in a context with distinct data domains: 1) train a model on one domain and deploy it across multiple distinct domains, 2) train multiple bespoke models that are optimized for deployment on individual domains, 3) train and deploy a single global model on all domains, and 4) train a model on one domain and adapt it through technical means to make it performant on a distinct domain. …”
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DEM error verified by airborne data.
Published 2024“…In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. …”
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A portfolio selection model based on the knapsack problem under uncertainty
Published 2019“…The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. …”
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S1 Data -
Published 2025“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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