Constraints in Weighted Averaging

Weighted averaging of classifier outputs is used in many MCSs, yet is still not well understood. Several empirical studies have investigated the effect that non-negativity and sum-one constraints have on the error rate of weighted averaging rules, but there is little theory available to understand the results. In this paper we study how constraints on the weights affect the location of the decision boundary of a MCS using weighted averaging. This allows us to explain many of the empirical findings, and suggest guidelines for when the application of constraints may or may not be appropriate. We also consider how these results relate to the analytical framework first proposed by Tumer and Ghosh [5].

• Type: Conference
• Year: 2009
• Where: MCS
• Authors: Amber Tomas
• Keywords: MCS 2009, Pattern Recognition, Several Empirical Studies, Weighted Averaging, Weighted Averaging Rules