In this paper, we propose a hybrid filter/wrapper approach for fast feature selection using the Residual Mutual Information (RMI) between the function approximator output and the remaining features as selection criterion. This approach can handle redundancies in the data as well as the bias of the employed learning machine while keeping the number of required training and evaluation procedures ...

Forward-backward selection is one of the most basic and commonly-used feature selection algorithms available. It is also general and conceptually applicable to many different types of data. In this paper, we propose a heuristic that significantly improves its running time, while preserving predictive accuracy. The idea is to temporarily discard the variables that are conditionally independent w...

The objective of modelling from data is not that the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, interpretability and ease for knowledge extraction. All these desired properties depend crucially on the ability to construct appropriate parsimonious models by the modelling process, and a basic principle in practical ...