Component-Wise Boosting of Targets for Multi-Output Prediction
This work addresses multi-output prediction problems where multiple targets of diverse types need to be predicted simultaneously, offering an incremental improvement in learning target dependencies.
The paper tackles multi-output prediction by introducing a problem transformation method that learns sparse, interpretable dependencies between target variables using component-wise boosting. The method achieves competitive performance on multi-label, multivariate regression, and mixed-type datasets compared to similar approaches.
Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algorithm that uses the problem transformation method for multi-output prediction, while simultaneously learning the dependencies between target variables in a sparse and interpretable manner. In a first step, predictions are obtained for each target individually. Target dependencies are then learned via a component-wise boosting approach. We compare our new method with similar approaches in a benchmark using multi-label, multivariate regression and mixed-type datasets.