2007 International Conference onMachine Learning and Applications (ICMLA '07), December 13-15, 2007, Cincinnati, Ohio, USA
model evaluation; data mining; machine learning; ROC; ROCCH; AUC; business gains; prognostics
Unexpected failures of complex equipment such as trains or aircraft introduce superfluous costs, disrupt operation, have an effect on consumer's satisfaction, and potentially decrease safety in practice. One of the objectives of Prognostics and Health Management (PHM) systems is to help reduce the number of unexpected failures by continuously monitoring the components of interest and predicting their failures sufficiently in advance to allow for proper planning. In other words, PHM systems may help turn unexpected failures into expected ones. Recent research has demonstrated the usefulness of data mining to help build prognostic models for PHM but also has identified the need for new model evaluation methods that take into account the specificities of prognostic applications. This paper investigates this problem. First, it reviews classical and recent methods to evaluate data mining models and it explains their deficiencies with respect to prognostic applications. The paper then proposes a novel approach that overcomes these deficiencies. This approach integrates the various costs and benefits involved in prognostics to quantify the cost saving expected from a given prognostic model. From the end user's perspective, the formula is practical as it is easy to understand and requires realistic inputs. The paper illustrates the usefulness of the methods through a real-world case study involving data-mining prognostic models and realistic costs/benefits information. The results show the feasibility of the approach and its applicability to various prognostic applications.
The Proceedings of the 2007 International Conference onMachine Learning and Applications (ICMLA '07).