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Found 5 papers, 4 papers with code
Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data
We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers.
Assessing binary classifiers using only positive and unlabeled data
We provide theoretical bounds on the quality of our estimates, illustrate the importance of estimating the fraction of positives in the unlabeled set and demonstrate empirically that we are able to reliably estimate ROC and PR curves on real data.
EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.
Fast Prediction with SVM Models Containing RBF Kernels
We present an approximation scheme for support vector machine models that use an RBF kernel.
A Robust Ensemble Approach to Learn From Positive and Unlabeled Data Using SVM Base Models
The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives.
