Cost-sensitive methods of constructing hierarchical classifiers



Abstract: The cost of a future exploitation of a decision support system plays a key role. The paper deals with the problem of feature value acquisition cost for such systems. We present a modification of a cost-sensitive learning method for decision-tree induction with fixed attribute acquisition cost limit. Properties of the concept are established during computer experiments conducted on chosen benchmark databases from the UCI Machine Learning Repository and a real medical decision task. The results of experiments confirm that, for some decision problems, our proposition allows us to obtain a classifier with the same quality as a classifier obtained without cost limit but its exploitation is cheaper.