An appropriate criterion for differentiating between various approaches is whether they target a specific predictive model or are model-independent. Many researchers are working to develop methods specific to the neural network model, but here, we focus on model-independent (i.e., “black box”) approaches.
Model-independent approaches are based on exploiting general supervised learning framework parameters such as learning sets and attributes. These approaches include observing how a particular learning example locally influences a model, conducting local error modelling, and utilizing other properties of the input domain such as its density distribution. They are defined independently of any predictive model formalization. This assures greater generalisability because it offers users more freedom to choose the predictive model that suits the problem best. However, the reliability estimates based on these approaches are usually not probabilistically interpretable, meaning that they can take values from an arbitrary interval of numbers and are therefore harder to analytically evaluate.
In the following, we summarize the work in three related research fields that has motivated the development of the model-independent approaches. Fields that deal with perturbed data and the use of unlabelled examples in supervised learning are generally concerned with accuracy performance and with evaluating the whole predictive model. Both of these fields exploit variations in the original learning set to improve general model accuracy. Some of these methods also focus on weighing and analysing the role of individual examples in learning set variation. One way to further apply this approach is to use transduction and sensitivity analysis as a general framework, as indicated in the last portion of this section.