Cost-sensitive splitting and selection method for medical decision support system

Abstract

The paper presents a cost-sensitive modification of the Adaptive Splitting and Selection (AdaSS) algorithm, which trains a combined classifier based on a feature space partitioning. In this study the algorithm considers constraints put on the cost of selected features, which are one of the key-problems in the clinical decision support systems. The modified version takes into consideration both the overall classification accuracy and the cost constraints, returning balanced solution for the problem at hand. Proposed method was evaluated on the basis of computer experiments run on cost-sensitive medical benchmark datasets. © 2012 Springer-Verlag.