Search Results for author:
Found 4 papers, 1 papers with code
To SMOTE, or not to SMOTE?
Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data.
Synthesising Multi-Modal Minority Samples for Tabular Data
Furthermore, the superior synthetic data yields better prediction quality in downstream binary classification tasks, as was demonstrated in extensive experiments with 27 publicly available real-world datasets
AE-SMOTE: A Multi-Modal Minority Oversampling Framework
Real-world binary classification tasks are in many cases unbalanced i. e. the minority class is much smaller than the majority class.
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
