A Bayesian Boosting Model
This work addresses binary classification with label noise, but it is incremental as it adapts existing boosting methods to a Bayesian setting.
The authors tackled binary classification by modeling label noise hierarchically within a Bayesian framework, resulting in VIBoost, a boosting-like algorithm that shows connections to AdaBoost and is validated on four datasets.
We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections to AdaBoost and give experimental results from four datasets.