Classification with test costs and background knowledge

We propose a novel approach to the problem of the classification with test costs understood as costs of obtaining attribute values of classified examples. Many existing approaches construct classifiers in order to control the tradeoff between test costs and the prediction accuracy (or misclassification costs). The aim of the proposed method is to reduce test costs while maintaining of the prediction accuracy of a classifier. We assume that attribute values are represented at different levels of abstraction and model domain background knowledge. Our approach sequentially explores these levels during classification – in each iteration it selects and conducts a test that precises the representation of a classified example (i.e., acquires an attribute value), invokes a naïve Bayes classifier for this new representation and checks the classifier’s outcome to decide whether this iterative process can be stopped. The selection of the test in each iteration takes into account the possible improvement of the prediction accuracy and the cost of this test. We show that the prediction accuracy obtained for classified examples represented precisely (i.e., when all the tests have been conducted and all specific attribute values have been acquired) can be achieved for a much smaller number of tests (i.e., when not all specific attribute values have been acquired). Moreover, we show that without levels of abstraction and with uniform test costs our method can be used for selecting features and it is competitive to popular feature selection schemes: filter and wrapper.