The following pages link to SMOTE (Q45948):
Displaying 50 items.
- Correcting classifiers for sample selection bias in two-phase case-control studies (Q1784139) (← links)
- CCR: a combined cleaning and resampling algorithm for imbalanced data classification (Q1787035) (← links)
- A novel SMOTE-based classification approach to online data imbalance problem (Q1793326) (← links)
- Combining experts in order to identify binding sites in yeast and mouse genomic data (Q1932034) (← links)
- Trimmed LASSO regression estimator for binary response data (Q1987665) (← links)
- Fused variable screening for massive imbalanced data (Q2008001) (← links)
- An instance-based learning recommendation algorithm of imbalance handling methods (Q2010579) (← links)
- Selective linearization for multi-block statistical learning (Q2030520) (← links)
- Option valuation under no-arbitrage constraints with neural networks (Q2030534) (← links)
- Handling imbalance in hierarchical classification problems using local classifiers approaches (Q2036779) (← links)
- An overlap sensitive neural network for class imbalanced data (Q2036784) (← links)
- LoRAS: an oversampling approach for imbalanced datasets (Q2051243) (← links)
- RCSMOTE: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem (Q2053858) (← links)
- Chebyshev approaches for imbalanced data streams regression models (Q2066647) (← links)
- VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams (Q2066665) (← links)
- Density-based weighting for imbalanced regression (Q2071357) (← links)
- RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification (Q2071485) (← links)
- RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods (Q2071508) (← links)
- Cautious classification based on belief functions theory and imprecise relabelling (Q2076976) (← links)
- Fault diagnosis of gear rotor system based on collaborative filtering recommendation method (Q2089136) (← links)
- Hybrid ResNet based on joint basic and attention modules for long-tailed classification (Q2092447) (← links)
- Machine learning algorithms for dengue risk assessment: a case study for São Luís do Maranhão (Q2099551) (← links)
- Prediction of brand stories spreading on social networks (Q2103849) (← links)
- Extending business failure prediction models with textual website content using deep learning (Q2106750) (← links)
- An ensemble tree classifier for highly imbalanced data classification (Q2121170) (← links)
- Assessing the data complexity of imbalanced datasets (Q2123543) (← links)
- RSMOTE: a self-adaptive robust SMOTE for imbalanced problems with label noise (Q2123573) (← links)
- A hybrid data-level ensemble to enable learning from highly imbalanced dataset (Q2124162) (← links)
- New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification (Q2136627) (← links)
- Uncertainty-aware resampling method for imbalanced classification using evidence theory (Q2146035) (← links)
- Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: an application to adolescent alcohol use with spatial covariates (Q2152395) (← links)
- Techniques to improve ecological interpretability of black-box machine learning models. Case study on biological health of streams in the United States with gradient boosted trees (Q2163504) (← links)
- Imbalanced learning for insurance using modified loss functions in tree-based models (Q2172025) (← links)
- Post-boosting of classification boundary for imbalanced data using geometric mean (Q2179087) (← links)
- Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (Q2184072) (← links)
- Data science applications to string theory (Q2187812) (← links)
- Growing regression tree forests by classification for continuous object pose estimation (Q2193539) (← links)
- Large-scale distributed sparse class-imbalance learning (Q2198074) (← links)
- A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty (Q2200656) (← links)
- Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series (Q2217398) (← links)
- Tree-based space partition and merging ensemble learning framework for imbalanced problems (Q2224911) (← links)
- Enhancing techniques for learning decision trees from imbalanced data (Q2228292) (← links)
- How training on multiple time slices improves performance in churn prediction (Q2239912) (← links)
- Correlation for tree-shaped datasets and its Bayesian estimation (Q2242179) (← links)
- Using pre \& post-processing methods to improve binding site predictions (Q2270825) (← links)
- A multi-objective optimisation approach for class imbalance learning (Q2275972) (← links)
- A dynamic over-sampling procedure based on sensitivity for multi-class problems (Q2275973) (← links)
- Classification tree algorithm for grouped variables (Q2282589) (← links)
- Response transformation and profit decomposition for revenue uplift modeling (Q2286984) (← links)
- Predictive models for bariatric surgery risks with imbalanced medical datasets (Q2288869) (← links)
