How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms
This work provides a framework for researchers and practitioners in diagnostic settings to systematically evaluate and choose algorithms, but it is incremental as it organizes existing methods rather than introducing new ones.
The authors proposed a taxonomy for diagnosis computation algorithms to standardize their assessment, classification, and comparison, enabling researchers and practitioners to easily select and compare techniques based on key properties.
This work proposes a taxonomy for diagnosis computation methods which allows their standardized assessment, classification and comparison. The aim is to (i) give researchers and practitioners an impression of the diverse landscape of available diagnostic techniques, (ii) allow them to easily retrieve the main features as well as pros and cons of the approaches, (iii) enable an easy and clear comparison of the techniques based on their characteristics wrt. a list of important and well-defined properties, and (iv) facilitate the selection of the "right" algorithm to adopt for a particular problem case, e.g., in practical diagnostic settings, for comparison in experimental evaluations, or for reuse, modification, extension, or improvement in the course of research.