Abstract: Fully taking into account the hints possibly hidden in the absent data, this paper proposes a new criterion when selecting attributes for splitting to build a decision tree for a given dataset. In our approach, it must pay a certain cost to obtain an attribute value. We also consider discounts in test costs when groups of attributes are tested together. When consumer offers finite resources, we can make the best use of the resources as well as optimal results obtained by the tree. In addition, we also put forward advice about whether it is worthy of increasing resources or not.