We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy. © Springer-Verlag Berlin Heidelberg 2007.