A hierarchical model for test-cost-sensitive decision systems

Cost-sensitive learning is an important issue in both data mining and machine learning, in that it deals with the problem of learning from decision systems relative to a variety of costs. In this paper, we introduce a hierarchy of cost-sensitive decision systems from a test cost perspective. Two major issues are addressed with regard to test cost dependency. The first is concerned with the common test cost, where a group of tests share a common cost, while the other relates to the sequence-dependent test cost, where the order of the test sequence influences the total cost. Theoretical aspects of each of the six models in our hierarchy are investigated and illustrated via examples. The proposed models are shown to be useful for exploring cost related information in various different applications.

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