On the use of Expected Attainable Discrimination for feature selection in large scale medical risk p
This report investigates the use of expected attainable discrimination (EAD) as a measure to select discrete valued features in two-class prediction problems. In essence, EAD tells us the performance we could expect to achieve with a simple histogram probability density model of a given dataset. For discrete valued features, this kind of density model is bias-free but can have large variance. Given insuucient training data, such a model's test set performance will be lower than that of a suitably biased model. In light of this, we explore the usefulness of EAD for feature selection.