Cost-sensitive classifier ensemble for medical decision support system

Multiple classifier systems are currently the focus of intense research. In this conceptual approach, the main effort focuses on establishing decision on the basis of a set of individual classifiers' outputs. This approach is well known but usually most of propositions do not take exploitation cost of such a classifier under consideration. The paper deals with the problem how to take a test acquisition cost during classification task under the framework of combined approach on board. The problem is known as cost-sensitive classification and it has been usually considered for the decision tree induction. In this work we adapt mentioned above idea into choosing members of classifier ensemble and propose a method of choosing a pool of individual classifiers which take into consideration on the one hand quality of ensemble on the other hand cost of classification. Properties of mentioned concept are established during computer experiments conducted on chosen medical benchmark databases from UCI Machine Learning Repository.