RELF: Robust Regression Extended with Ensemble Loss Function
This work addresses robust regression for machine learning practitioners, offering an incremental improvement through ensemble techniques.
The paper tackles the problem of improving regression performance in noisy environments by proposing an ensemble loss function applied to a simple regressor, resulting in significant performance gains compared to state-of-the-art methods as demonstrated in experiments.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regressor. We then propose a half-quadratic learning algorithm in order to find the parameter of the regressor and the optimal weights associated with each loss function. Moreover, we show that our proposed loss function is robust in noisy environments. For a particular class of loss functions, we show that our proposed ensemble loss function is Bayes consistent and robust. Experimental evaluations on several datasets demonstrate that our proposed ensemble loss function significantly improves the performance of a simple regressor in comparison with state-of-the-art methods.