Using Feature Selection to Find Inputs that Work Better as Extra Outputs
In supervised learning there is usually a clear distinction between inputs and outputs — inputs are what you measure, outputs are what you predict from those measurements. The distinction between inputs and outputs is not this simple. Previously, we demonstrated that on synthetic problems some input features are more useful when used as extra outputs than when used as inputs[6]. This paper shows the same effect on a real problem, and presents a means of determining what features can be used as extra outputs. We show that the feature selection method devised by Koller and Sahami[ll] can be used to select features to use as extra outputs, and that using some features as as extra outputs instead of as inputs yields better performance on the DNA splice-junction domain.