Boundary Methods (BMs) are a collection of tools used for distribution analysis. This paper explores the theoretical and complexity issues associated with using BMs for Feature Set Evaluation (FSE). First we show the theoretical relationship between Overlap Sum (OS), the BM measure of class separability, and Bayes error (e). This relationship demonstrates the utility of using BMs for FSE. Next, we investigate complexity issues associated with using BMs for FSE and compare with other techniques used for FSE.